NIPS 2018 paper list（论文列表）

Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization Francis Bach
Structure-Aware Convolutional Neural Networks Jianlong Chang, Jie Gu, Lingfeng Wang, GAOFENG MENG, SHIMING XIANG, Chunhong Pan
Kalman Normalization: Normalizing Internal Representations Across Network Layers Guangrun Wang, jiefeng peng, Ping Luo, Xinjiang Wang, Liang Lin
HOGWILD!-Gibbs can be PanAccurate Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language Seonghyeon Nam, Yunji Kim, Seon Joo Kim
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis Huaibo Huang, zhihang li, Ran He, Zhenan Sun, Tieniu Tan
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with beta-Divergences Jeremias Knoblauch, Jack E. Jewson, Theodoros Damoulas
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning Tyler Scott, Karl Ridgeway, Michael C. Mozer
Generalized Inverse Optimization through Online Learning Chaosheng Dong, Yiran Chen, Bo Zeng
An Off-policy Policy Gradient Theorem Using Emphatic Weightings Ehsan Imani, Eric Graves, Martha White
Supervised autoencoders: Improving generalization performance with unsupervised regularizers Lei Le, Andrew Patterson, Martha White
Visual Object Networks: Image Generation with Disentangled 3D Representations Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, Bill Freeman
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units Yixi Xu, Xiao Wang
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing
Learning long-range spatial dependencies with horizontal gated recurrent units Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution Zhisheng Zhong, Tiancheng Shen, Yibo Yang, Zhouchen Lin, Chao Zhang
Fast Similarity Search via Optimal Sparse Lifting Wenye Li, Jingwei Mao, Yin Zhang, Shuguang Cui
Learning Deep Disentangled Embeddings With the F-Statistic Loss Karl Ridgeway, Michael C. Mozer
Geometrically Coupled Monte Carlo Sampling Mark Rowland, Krzysztof M. Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
An Efficient Pruning Algorithm for Robust Isotonic Regression Cong Han Lim
PAC-learning in the presence of adversaries Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
Sparse DNNs with Improved Adversarial Robustness Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen
Snap ML: A Hierarchical Framework for Machine Learning Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi, Madhusudanan Kandasamy, Haralampos Pozidis
See and Think: Disentangling Semantic Scene Completion Shice Liu, YU HU, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li
Chain of Reasoning for Visual Question Answering Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong
Sigsoftmax: Reanalysis of the Softmax Bottleneck Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, Ming-Hsuan Yang
Probabilistic Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic
MetaAnchor: Learning to Detect Objects with Customized Anchors Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun
Image Inpainting via Generative Multi-column Convolutional Neural Networks Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia
On Misinformation Containment in Online Social Networks Amo Tong, Ding-Zhu Du, Weili Wu
A^2-Nets: Double Attention Networks Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng
Self-Supervised Generation of Spatial Audio for 360° Video Pedro Morgado, Nuno Nvasconcelos, Timothy Langlois, Oliver Wang
How Many Samples are Needed to Estimate a Convolutional Neural Network? Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan R. Salakhutdinov, Aarti Singh
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced Simon S. Du, Wei Hu, Jason D. Lee
Optimization for Approximate Submodularity Yaron Singer, Avinatan Hassidim
(Probably) Concave Graph Matching Haggai Maron, Yaron Lipman
Deep Defense: Training DNNs with Improved Adversarial Robustness Ziang Yan, Yiwen Guo, Changshui Zhang
Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes Junqi Tang, Mohammad Golbabaee, Francis Bach, Mike E. davies
Implicit Reparameterization Gradients Mikhail Figurnov, Shakir Mohamed, Andriy Mnih
Training DNNs with Hybrid Block Floating Point Mario Drumond, Tao LIN, Martin Jaggi, Babak Falsafi
A Model for Learned Bloom Filters and Optimizing by Sandwiching Michael Mitzenmacher
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions Minhyuk Sung, Hao Su, Ronald Yu, Leonidas J. Guibas
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling Yunzhe Tao, Qi Sun, Qiang Du, Wei Liu
Are ResNets Provably Better than Linear Predictors? Ohad Shamir
Learning to Decompose and Disentangle Representations for Video Prediction Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F. Fei-Fei, Juan Carlos Niebles
Multi-Task Learning as Multi-Objective Optimization Ozan Sener, Vladlen Koltun
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search Zhuwen Li, Qifeng Chen, Vladlen Koltun
Self-Erasing Network for Integral Object Attention Qibin Hou, PengTao Jiang, Yunchao Wei, Ming-Ming Cheng
LinkNet: Relational Embedding for Scene Graph Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
How to Start Training: The Effect of Initialization and Architecture Boris Hanin, David Rolnick
Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? Boris Hanin
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, Payel Das
HitNet: Hybrid Ternary Recurrent Neural Network Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang, Yuan Xie
A Unified Framework for Extensive-Form Game Abstraction with Bounds Christian Kroer, Tuomas Sandholm
Removing the Feature Correlation Effect of Multiplicative Noise Zijun Zhang, Yining Zhang, Zongpeng Li
Maximum-Entropy Fine Grained Classification Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
On Learning Markov Chains Yi HAO, Alon Orlitsky, Venkatadheeraj Pichapati
A Neural Compositional Paradigm for Image Captioning Bo Dai, Sanja Fidler, Dahua Lin
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
Dialog-based Interactive Image Retrieval Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Feris
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
Are GANs Created Equal? A Large-Scale Study Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Learning Disentangled Joint Continuous and Discrete Representations Emilien Dupont
TADAM: Task dependent adaptive metric for improved few-shot learning Boris Oreshkin, Pau Rodríguez López, Alexandre Lacoste
Do Less, Get More: Streaming Submodular Maximization with Subsampling Moran Feldman, Amin Karbasi, Ehsan Kazemi
Sparse Covariance Modeling in High Dimensions with Gaussian Processes Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne Haake
Deep Neural Nets with Interpolating Function as Output Activation Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley Osher
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang
Visual Memory for Robust Path Following
KDGAN: Knowledge Distillation with Generative Adversarial Networks Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi
Long short-term memory and Learning-to-learn in networks of spiking neurons Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian
Informative Features for Model Comparison Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton
PointCNN: Convolution On X-Transformed Points Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen
Connectionist Temporal Classification with Maximum Entropy Regularization Hu Liu, Sheng Jin, Changshui Zhang
Large Margin Deep Networks for Classification Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio
Generalizing Graph Matching beyond Quadratic Assignment Model Tianshu Yu, Junchi Yan, Yilin Wang, Wei Liu, baoxin Li
Solving Large Sequential Games with the Excessive Gap Technique Christian Kroer, Gabriele Farina, Tuomas Sandholm
Discrimination-aware Channel Pruning for Deep Neural Networks Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jinhui Zhu
On the Dimensionality of Word Embedding
Reinforced Continual Learning Ju Xu, Zhanxing Zhu
Uncertainty-Aware Attention for Reliable Interpretation and Prediction Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang
DropMax: Adaptive Variational Softmax Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang
Posterior Concentration for Sparse Deep Learning Veronika Rockova, nicholas polson
A flexible model for training action localization with varying levels of supervision Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents YAN ZHENG, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited Di Wang, Marco Gaboardi, Jinhui Xu
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
Learning semantic similarity in a continuous space Michel Deudon
MetaReg: Towards Domain Generalization using Meta-Regularization Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa
Boosted Sparse and Low-Rank Tensor Regression Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang
Domain-Invariant Projection Learning for Zero-Shot Recognition An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Josh Tenenbaum
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong
Quadratic Decomposable Submodular Function Minimization Pan Li, Niao He, Olgica Milenkovic
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
ell_1-regression with Heavy-tailed Distributions Lijun Zhang, Zhi-Hua Zhou
Neural Nearest Neighbors Networks Tobias Plötz, Stefan Roth
Efficient nonmyopic batch active search Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett
A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers Omer Ben-Porat, Moshe Tennenholtz
Interactive Structure Learning with Structural Query-by-Committee Christopher Tosh, Sanjoy Dasgupta
Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere Yanjun Li, Yoram Bresler
Video-to-Video Synthesis Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Nikolai Yakovenko, Andrew Tao, Jan Kautz, Bryan Catanzaro
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD Zeyuan Allen-Zhu
Synthesize Policies for Transfer and Adaptation across Tasks and Environments Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha
Adversarial vulnerability for any classifier Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
Evolution-Guided Policy Gradient in Reinforcement Learning Shauharda Khadka, Kagan Tumer
Toddler-Inspired Visual Object Learning Sven Bambach, David Crandall, Linda Smith, Chen Yu
Alternating optimization of decision trees, with application to learning sparse oblique trees Miguel A. Carreira-Perpinan, Pooya Tavallali
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, hongsheng Li
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity Pan Zhou, Xiaotong Yuan, Jiashi Feng
The Lingering of Gradients: How to Reuse Gradients Over Time Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang
Unsupervised Learning of View-invariant Action Representations Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making Hoda Heidari, Claudio Ferrari, Krishna Gummadi, Andreas Krause
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks Qilong Wang, Zilin Gao, Jiangtao Xie, Wangmeng Zuo, Peihua Li
Image-to-image translation for cross-domain disentanglement Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection Taylor Mordan, Nicolas THOME, Gilles Henaff, Matthieu Cord
Adaptive Online Learning in Dynamic Environments Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
FRAGE: Frequency-Agnostic Word Representation Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu
Generative Neural Machine Translation Harshil Shah, David Barber
Found Graph Data and Planted Vertex Covers Austin R. Benson, Jon Kleinberg
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding Hajin Shim, Sung Ju Hwang, Eunho Yang
Regularization Learning Networks: Deep Learning for Tabular Datasets Ira Shavitt, Eran Segal
Multitask Boosting for Survival Analysis with Competing Risks Alexis Bellot, Mihaela van der Schaar
Geometry Based Data Generation Ofir Lindenbaum, Jay Stanley, Guy Wolf, Smita Krishnaswamy
SLAYER: Spike Layer Error Reassignment in Time Sumit Bam Shrestha, Garrick Orchard
On Oracle-Efficient PAC RL with Rich Observations Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Gradient Descent for Spiking Neural Networks Dongsung Huh, Terrence J. Sejnowski
Generalizing Tree Probability Estimation via Bayesian Networks Cheng Zhang, Frederick A Matsen IV
Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior Sid Reddy, Anca Dragan, Sergey Levine
Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue
A loss framework for calibrated anomaly detection
PacGAN: The power of two samples in generative adversarial networks Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh
Variational Memory Encoder-Decoder Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities Yunwen Lei, Ke Tang
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Hybrid Knowledge Routed Modules for Large-scale Object Detection ChenHan Jiang, Hang Xu, Xiaodan Liang, Liang Lin
Bilinear Attention Networks Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, Qi Wu
Multi-Class Learning: From Theory to Algorithm Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
Multivariate Time Series Imputation with Generative Adversarial Networks Yonghong Luo, Xiangrui Cai, Ying ZHANG, Jun Xu, Yuan xiaojie
Learning Versatile Filters for Efficient Convolutional Neural Networks Yunhe Wang, Chang Xu, Chunjing XU, Chao Xu, Dacheng Tao
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization Robert Gower, Filip Hanzely, Peter Richtarik, Sebastian U. Stich
DifNet: Semantic Segmentation by Diffusion Networks Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen
Conditional Adversarial Domain Adaptation Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Michael I. Jordan
Neighbourhood Consensus Networks Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic
Relating Leverage Scores and Density using Regularized Christoffel Functions Edouard Pauwels, Francis Bach, Jean-Philippe Vert
Non-Local Recurrent Network for Image Restoration Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang
Bayesian Semi-supervised Learning with Graph Gaussian Processes Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
Foreground Clustering for Joint Segmentation and Localization in Videos and Images Abhishek Sharma
Video Prediction via Selective Sampling Jingwei Xu, Bingbing Ni, Xiaokang Yang
Distilled Wasserstein Learning for Word Embedding and Topic Modeling Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin
Learning to Exploit Stability for 3D Scene Parsing Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu
Neural Guided Constraint Logic Programming for Program Synthesis Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Genetic-Gated Networks for Deep Reinforcement Learning Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning Romain WARLOP, Alessandro Lazaric, Jérémie Mary
Enhancing the Accuracy and Fairness of Human Decision Making Isabel Valera, Adish Singla, Manuel Gomez Rodriguez
Temporal Regularization for Markov Decision Process Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning Jesse Krijthe, Marco Loog
Simple random search of static linear policies is competitive for reinforcement learning Horia Mania, Aurelia Guy, Benjamin Recht
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan
Entropy and mutual information in models of deep neural networks Marylou Gabrié, Andre Manoel, Clément Luneau, jean barbier, Nicolas Macris, Florent Krzakala, Lenka Zdeborová
Collaborative Learning for Deep Neural Networks Guocong Song, Wei Chai
High Dimensional Linear Regression using Lattice Basis Reduction Ilias Zadik, David Gamarnik
Symbolic Graph Reasoning Meets Convolutions Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors Arash Vahdat, Evgeny Andriyash, William Macready
Partially-Supervised Image Captioning Peter Anderson, Stephen Gould, Mark Johnson
3D-Aware Scene Manipulation via Inverse Graphics Shunyu Yao, Tzu Ming Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, Bill Freeman, Josh Tenenbaum
Random Feature Stein Discrepancies Jonathan Huggins, Lester Mackey
Distributed Stochastic Optimization via Adaptive SGD Ashok Cutkosky, Róbert Busa-Fekete
Precision and Recall for Time Series Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
Deep Attentive Tracking via Reciprocative Learning Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang
Virtual Class Enhanced Discriminative Embedding Learning Binghui Chen, Weihong Deng, Haifeng Shen
Attention in Convolutional LSTM for Gesture Recognition Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun
Pelee: A Real-Time Object Detection System on Mobile Devices Jun Wang, Tanner Bohn, Charles Ling
Universal Growth in Production Economies Simina Branzei, Ruta Mehta, Noam Nisan
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors Fei Jiang, Guosheng Yin, Francesca Dominici
Efficient Stochastic Gradient Hard Thresholding Pan Zhou, Xiaotong Yuan, Jiashi Feng
SplineNets: Continuous Neural Decision Graphs Cem Keskin, Shahram Izadi
Generalized Zero-Shot Learning with Deep Calibration Network Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan
Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P. Xing
Embedding Logical Queries on Knowledge Graphs Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec
Learning Optimal Reserve Price against Non-myopic Bidders Jinyan Liu, Zhiyi Huang, Xiangning Wang
Sequential Context Encoding for Duplicate Removal Lu Qi, Shu Liu, Jianping Shi, Jiaya Jia
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Nonparametric learning from Bayesian models with randomized objective functions Simon Lyddon, Stephen Walker, Chris C. Holmes
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely, Konstantin Mishchenko, Peter Richtarik
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector Amit Zohar, Lior Wolf
One-Shot Unsupervised Cross Domain Translation Sagie Benaim, Lior Wolf
Regularizing by the Variance of the Activations’ Sample-Variances Etai Littwin, Lior Wolf
Overlapping Clustering Models, and One (class) SVM to Bind Them All Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann
DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang
Norm matters: efficient and accurate normalization schemes in deep networks Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, Rene Vidal, Manolis Tsakiris
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval Helena Peic Tukuljac, Antoine Deleforge, Remi Gribonval
Mixture Matrix Completion Daniel Pimentel-Alarcon
Trajectory Convolution for Action Recognition Yue Zhao, Yuanjun Xiong, Dahua Lin
The Description Length of Deep Learning models Léonard Blier, Yann Ollivier
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem Sampath Kannan, Jamie H. Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Revisiting Decomposable Submodular Function Minimization with Incidence Relations Pan Li, Olgica Milenkovic
A Practical Algorithm for Distributed Clustering and Outlier Detection Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang
Learning to Reconstruct Shapes from Unseen Classes
BourGAN: Generative Networks with Metric Embeddings Chang Xiao, Peilin Zhong, Changxi Zheng
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning Ofir Marom, Benjamin Rosman
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Mikhail Belkin, Daniel J. Hsu, Partha Mitra
Breaking the Span Assumption Yields Fast Finite-Sum Minimization Robert Hannah, Yanli Liu, Daniel O’Connor, Wotao Yin
Structured Local Minima in Sparse Blind Deconvolution Yuqian Zhang, Han-wen Kuo, John Wright
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data Xenia Miscouridou, Francois Caron, Yee Whye Teh
Non-monotone Submodular Maximization in Exponentially Fewer Iterations Eric Balkanski, Adam Breuer, Yaron Singer
MetaGAN: An Adversarial Approach to Few-Shot Learning Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
Local Differential Privacy for Evolving Data Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner
Gaussian Process Conditional Density Estimation Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth
Meta-Gradient Reinforcement Learning Zhongwen Xu, Hado P. van Hasselt, David Silver
Modular Networks: Learning to Decompose Neural Computation Louis Kirsch, Julius Kunze, David Barber
Learning to Navigate in Cities Without a Map Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, koray kavukcuoglu, Andrew Zisserman, Raia Hadsell
Query Complexity of Bayesian Private Learning Kuang Xu
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization Cedric Josz, Yi Ouyang, Richard Zhang, Javad Lavaei, Somayeh Sojoudi
Recurrent World Models Facilitate Policy Evolution
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling Shannon McCurdy
Wasserstein Variational Inference Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris
How Does Batch Normalization Help Optimization?
Verifiable Reinforcement Learning via Policy Extraction Osbert Bastani, Yewen Pu, Armando Solar-Lezama
Leveraged volume sampling for linear regression Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
Model Agnostic Supervised Local Explanations Gregory Plumb, Denali Molitor, Ameet S. Talwalkar
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication Peng Jiang, Gagan Agrawal
Active Learning for Non-Parametric Regression Using Purely Random Trees Jack Goetz, Ambuj Tewari, Paul Zimmerman
Tree-to-tree Neural Networks for Program Translation Xinyun Chen, Chang Liu, Dawn Song
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks Hyeonseob Nam, Hyo-Eun Kim
Structural Causal Bandits: Where to Intervene? Sanghack Lee, Elias Bareinboim
Answerer in Questioner’s Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang
Online Learning with an Unknown Fairness Metric Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
Isolating Sources of Disentanglement in Variational Autoencoders
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms Dylan J. Foster, Akshay Krishnamurthy
Representation Learning for Treatment Effect Estimation from Observational Data Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang
Representation Balancing MDPs for Off-policy Policy Evaluation Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing
Causal Discovery from Discrete Data using Hidden Compact Representation Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao
Natasha 2: Faster Non-Convex Optimization Than SGD Zeyuan Allen-Zhu
Minimax Statistical Learning with Wasserstein distances Jaeho Lee, Maxim Raginsky
Provable Variational Inference for Constrained Log-Submodular Models Josip Djolonga, Stefanie Jegelka, Andreas Krause
Learning Hierarchical Semantic Image Manipulation through Structured Representations Seunghoon Hong, Xinchen Yan, Thomas S. Huang, Honglak Lee
Processing of missing data by neural networks Marek Śmieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek
Safe Active Learning for Time-Series Modeling with Gaussian Processes Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Computing Higher Order Derivatives of Matrix and Tensor Expressions Soeren Laue, Matthias Mitterreiter, Joachim Giesen
Paraphrasing Complex Network: Network Compression via Factor Transfer Jangho Kim, Seonguk Park, Nojun Kwak
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net Tom Michoel
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation Chaitanya Ryali, Gautam Reddy, Angela J. Yu
Empirical Risk Minimization Under Fairness Constraints Michele Donini, Luca Oneto, Shai Ben-David, John S. Shawe-Taylor, Massimiliano Pontil
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds Eldar Insafutdinov, Alexey Dosovitskiy
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation Zhiqiang Xu
Factored Bandits Julian Zimmert, Yevgeny Seldin
Delta-encoder: an effective sample synthesis method for few-shot object recognition Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun
Mirrored Langevin Dynamics Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher
Moonshine: Distilling with Cheap Convolutions Elliot J. Crowley, Gavin Gray, Amos J. Storkey
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Adaptation to Easy Data in Prediction with Limited Advice Tobias Thune, Yevgeny Seldin
Differentially Private Bayesian Inference for Exponential Families Garrett Bernstein, Daniel R. Sheldon
Playing hard exploration games by watching YouTube Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, Nando de Freitas
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin
Norm-Ranging LSH for Maximum Inner Product Search Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng
Optimization over Continuous and Multi-dimensional Decisions with Observational Data Dimitris Bertsimas, Christopher McCord
Fast Estimation of Causal Interactions using Wold Processes Flavio Figueiredo, Guilherme Resende Borges, Pedro O.S. Vaz de Melo, Renato Assunção
When do random forests fail? Cheng Tang, Damien Garreau, Ulrike von Luxburg
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Optimistic optimization of a Brownian Jean-Bastien Grill, Michal Valko, Remi Munos
Practical Methods for Graph Two-Sample Testing Debarghya Ghoshdastidar, Ulrike von Luxburg
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport Lénaïc Chizat, Francis Bach
Constructing Deep Neural Networks by Bayesian Network Structure Learning Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
Weakly Supervised Dense Event Captioning in Videos Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang
Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood
From Stochastic Planning to Marginal MAP Hao Cui, Radu Marinescu, Roni Khardon
On Binary Classification in Extreme Regions Hamid JALALZAI, Stephan Clémençon, Anne Sabourin
Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models Yining Wang, Xi Chen, Yuan Zhou
Q-learning with Nearest Neighbors Devavrat Shah, Qiaomin Xie
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
Asymptotic optimality of adaptive importance sampling François Portier, Bernard Delyon
Learning latent variable structured prediction models with Gaussian perturbations Kevin Bello, Jean Honorio
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal Jiantao Jiao, Weihao Gao, Yanjun Han
Deep Reinforcement Learning of Marked Temporal Point Processes Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy, Lance Kaplan, Melih Kandemir
Parsimonious Bayesian deep networks Mingyuan Zhou
Single-Agent Policy Tree Search With Guarantees Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
Semi-crowdsourced Clustering with Deep Generative Models Yucen Luo, TIAN TIAN, Jiaxin Shi, Jun Zhu, Bo Zhang
The committee machine: Computational to statistical gaps in learning a two-layers neural network Benjamin Aubin, Antoine Maillard, jean barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms Avital Oliver, Augustus Odena, Colin A. Raffel, Ekin Dogus Cubuk, Ian Goodfellow
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward Lixing Chen, Jie Xu, Zhuo Lu
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Re-evaluating evaluation David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres Oisín Moran, Piergiorgio Caramazza, Daniele Faccio, Roderick Murray-Smith
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort
Data-Efficient Hierarchical Reinforcement Learning Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, Sergey Levine
Speaker-Follower Models for Vision-and-Language Navigation Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, Trevor Darrell
Inequity aversion improves cooperation in intertemporal social dilemmas Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez-Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch
Probabilistic Matrix Factorization for Automated Machine Learning Nicolo Fusi, Rishit Sheth, Melih Elibol
Stochastic Spectral and Conjugate Descent Methods Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov, Elnur Gasanov
Recurrent Relational Networks Rasmus Palm, Ulrich Paquet, Ole Winther
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Learning to Optimize Tensor Programs Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Boosting Black Box Variational Inference Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch
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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling
Step Size Matters in Deep Learning Kamil Nar, Shankar Sastry
Derivative Estimation in Random Design Yu Liu, Kris De Brabanter
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates Krishnakumar Balasubramanian, Saeed Ghadimi
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments Daniel Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation JING LI, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet
Infinite-Horizon Gaussian Processes Arno Solin, James Hensman, Richard E. Turner
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
Sequence-to-Segment Networks for Segment Detection Zijun Wei, Boyu Wang, Minh Hoai Nguyen, Jianming Zhang, Zhe Lin, Xiaohui Shen, Radomir Mech, Dimitris Samaras
Scaling the Poisson GLM to massive neural datasets through polynomial approximations David Zoltowski, Jonathan W. Pillow
Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games Yun Kuen Cheung
Why Is My Classifier Discriminatory? Irene Chen, Fredrik D. Johansson, David Sontag
Multi-Layered Gradient Boosting Decision Trees Ji Feng, Yang Yu, Zhi-Hua Zhou
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Communication Efficient Parallel Algorithms for Optimization on Manifolds Bayan Saparbayeva, Michael Zhang, Lizhen Lin
Neural Code Comprehension: A Learnable Representation of Code Semantics Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights Jiecao Chen, Qin Zhang, Yuan Zhou
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski
Modern Neural Networks Generalize on Small Data Sets Matthew Olson, Abraham Wyner, Richard Berk
Escaping Saddle Points in Constrained Optimization Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie
Adversarial Attacks on Stochastic Bandits Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Jerry Zhu
Optimal Subsampling with Influence Functions Daniel Ting, Eric Brochu
A Bandit Approach to Sequential Experimental Design with False Discovery Control Kevin G. Jamieson, Lalit Jain
Equality of Opportunity in Classification: A Causal Approach Junzhe Zhang, Elias Bareinboim
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao
Unsupervised Attention-guided Image-to-Image Translation Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim
Inferring Networks From Random Walk-Based Node Similarities Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis
NEON2: Finding Local Minima via First-Order Oracles Zeyuan Allen-Zhu, Yuanzhi Li
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, Lisa Amini
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting Hippolyt Ritter, Aleksandar Botev, David Barber
DeepProbLog: Neural Probabilistic Logic Programming Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property Yi Zhou, Zhe Wang, Yingbin Liang
Direct Estimation of Differences in Causal Graphs Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
Sublinear Time Low-Rank Approximation of Distance Matrices Ainesh Bakshi, David Woodruff
Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms Ganesh Sundaramoorthi, Anthony Yezzi
Bayesian Inference of Temporal Task Specifications from Demonstrations Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li
Data center cooling using model-predictive control Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu, Greg Imwalle
Acceleration through Optimistic No-Regret Dynamics Jun-Kun Wang, Jacob D. Abernethy
Lipschitz regularity of deep neural networks: analysis and efficient estimation Aladin Virmaux, Kevin Scaman
Minimax Estimation of Neural Net Distance Kaiyi Ji, Yingbin Liang
Leveraging the Exact Likelihood of Deep Latent Variable Models Pierre-Alexandre Mattei, Jes Frellsen
Bipartite Stochastic Block Models with Tiny Clusters Stefan Neumann
Learning sparse neural networks via sensitivity-driven regularization Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
Direct Runge-Kutta Discretization Achieves Acceleration Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization Dongruo Zhou, Pan Xu, Quanquan Gu
Faster Neural Networks Straight from JPEG Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski
TopRank: A practical algorithm for online stochastic ranking Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvari
Learning from discriminative feature feedback Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato
RetGK: Graph Kernels based on Return Probabilities of Random Walks Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai
Deep Generative Markov State Models Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Early Stopping for Nonparametric Testing Meimei Liu, Guang Cheng
Solving Non-smooth Constrained Programs with Lower Complexity than mathcal{O}(1/varepsilon): A Primal-Dual Homotopy Smoothing Approach Xiaohan Wei, Hao Yu, Qing Ling, Michael Neely
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks Joshua Fromm, Shwetak Patel, Matthai Philipose
Unsupervised Learning of Object Landmarks through Conditional Image Generation Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Probabilistic Neural Programmed Networks for Scene Generation Zhiwei Deng, Jiacheng Chen, YIFANG FU, Greg Mori
The streaming rollout of deep networks - towards fully model-parallel execution Volker Fischer, Jan Koehler, Thomas Pfeil
KONG: Kernels for ordered-neighborhood graphs Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
GumBolt: Extending Gumbel trick to Boltzmann priors Amir H. Khoshaman, Mohammad Amin
Neural Networks Trained to Solve Differential Equations Learn General Representations Martin Magill, Faisal Qureshi, Hendrick de Haan
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account Chaitanya Ryali, Angela J. Yu
Distributed Weight Consolidation: A Brain Segmentation Case Study Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira
Efficient Projection onto the Perfect Phylogeny Model Bei Jia, Surjyendu Ray, Sam Safavi, José Bento
TETRIS: TilE-matching the TRemendous Irregular Sparsity Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru
Differentially Private Robust Low-Rank Approximation Raman Arora, Vladimir braverman, Jalaj Upadhyay
Meta-Learning MCMC Proposals Tongzhou Wang, YI WU, Dave Moore, Stuart J. Russell
An Information-Theoretic Analysis for Thompson Sampling with Many Actions Shi Dong, Benjamin Van Roy
Flexible and accurate inference and learning for deep generative models Eszter Vértes, Maneesh Sahani
The Price of Privacy for Low-rank Factorization Jalaj Upadhyay
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
Bilevel Distance Metric Learning for Robust Image Recognition Jie Xu, Lei Luo, Cheng Deng, Heng Huang
Differentially Private Uniformly Most Powerful Tests for Binomial Data Jordan Awan, Aleksandra Slavković
Scalable Coordinated Exploration in Concurrent Reinforcement Learning Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu
Inexact trust-region algorithms on Riemannian manifolds Hiroyuki Kasai, Bamdev Mishra
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? Nitin Bansal, Xiaohan Chen, Zhangyang Wang
Binary Rating Estimation with Graph Side Information Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh
SimplE Embedding for Link Prediction in Knowledge Graphs Seyed Mehran Kazemi, David Poole
Differentially Private Contextual Linear Bandits Roshan Shariff, Or Sheffet
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing Abram L. Friesen, Pedro M. Domingos
A Bridging Framework for Model Optimization and Deep Propagation Risheng Liu, Shichao Cheng, xiaokun liu, Long Ma, Xin Fan, Zhongxuan Luo
Completing State Representations using Spectral Learning Nan Jiang, Alex Kulesza, Satinder Singh
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Adding One Neuron Can Eliminate All Bad Local Minima SHIYU LIANG, Ruoyu Sun, Jason D. Lee, R. Srikant
Mean-field theory of graph neural networks in graph partitioning Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi
The Physical Systems Behind Optimization Algorithms Lin Yang, Raman Arora, Vladimir braverman, Tuo Zhao
Mallows Models for Top-k Lists Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi
Amortized Inference Regularization Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Maximum Causal Tsallis Entropy Imitation Learning Kyungjae Lee, Sungjoon Choi, Songhwai Oh
Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives Song Zhou, Swati Gupta, Madeleine Udell
Semi-Supervised Learning with Declaratively Specified Entropy Constraints Haitian Sun, William W. Cohen, Lidong Bing
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E
Sparsified SGD with Memory Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
Exponentiated Strongly Rayleigh Distributions Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka
Importance Weighting and Variational Inference Justin Domke, Daniel R. Sheldon
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis Ye Jia, Yu Zhang, Ron Weiss, Quan Wang, Jonathan Shen, Fei Ren, zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu
Expanding Holographic Embeddings for Knowledge Completion Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal
Lifelong Inverse Reinforcement Learning Jorge Armando Mendez Mendez, Shashank Shivkumar, Eric Eaton
Explaining Deep Learning Models – A Bayesian Non-parametric Approach Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima Yaodong Yu, Pan Xu, Quanquan Gu
COLA: Decentralized Linear Learning Lie He, An Bian, Martin Jaggi
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare Edward Choi, Cao Xiao, Walter Stewart, Jimeng Sun
Adaptive Sampling Towards Fast Graph Representation Learning Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang
Hunting for Discriminatory Proxies in Linear Regression Models Samuel Yeom, Anupam Datta, Matt Fredrikson
Towards Robust Detection of Adversarial Examples Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
Active Matting Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin Yin, Rynson Lau
Learning filter widths of spectral decompositions with wavelets Haidar Khan, Bulent Yener
Byzantine Stochastic Gradient Descent Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
Spectral Filtering for General Linear Dynamical Systems
On Learning Intrinsic Rewards for Policy Gradient Methods Zeyu Zheng, Junhyuk Oh, Satinder Singh
Boolean Decision Rules via Column Generation Sanjeeb Dash, Oktay Gunluk, Dennis Wei
Adversarial Text Generation via Feature-Mover’s Distance Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Jing Rong, Tianbao Yang
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels Shahin Shahrampour, Vahid Tarokh
A Mathematical Model For Optimal Decisions In A Representative Democracy Malik Magdon-Ismail, Lirong Xia
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making Nishant Desai, Andrew Critch, Stuart J. Russell
Non-metric Similarity Graphs for Maximum Inner Product Search Stanislav Morozov, Artem Babenko
Recurrently Controlled Recurrent Networks Yi Tay, Anh Tuan Luu, Siu Cheung Hui
Fast greedy algorithms for dictionary selection with generalized sparsity constraints Kaito Fujii, Tasuku Soma
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
A Smoother Way to Train Structured Prediction Models Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui
Context-dependent upper-confidence bounds for directed exploration Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White
A Unified View of Piecewise Linear Neural Network Verification Rudy R. Bunel, Ilker Turkaslan, Philip Torr, Pushmeet Kohli, Pawan K. Mudigonda
Hierarchical Graph Representation Learning with Differentiable Pooling Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec
Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates Quoc Tran Dinh
Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces Boyla Mainsah, Dmitry Kalika, Leslie Collins, Siyuan Liu, Chandra Throckmorton
Porcupine Neural Networks: Approximating Neural Network Landscapes Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Fairness Through Computationally-Bounded Awareness Michael Kim, Omer Reingold, Guy Rothblum
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
Is Q-Learning Provably Efficient? Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang, Armando Solar-Lezama, Rishabh Singh
Measures of distortion for machine learning Leena Chennuru Vankadara, Ulrike von Luxburg
On the Local Minima of the Empirical Risk Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Densely Connected Attention Propagation for Reading Comprehension Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su
Bandit Learning with Positive Externalities Virag Shah, Jose Blanchet, Ramesh Johari
Learning Confidence Sets using Support Vector Machines Wenbo Wang, Xingye Qiao
Efficient Neural Network Robustness Certification with General Activation Functions Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney
Neural Edit Operations for Biological Sequences Satoshi Koide, Keisuke Kawano, Takuro Kutsuna
Objective and efficient inference for couplings in neuronal networks Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima
Learning from Group Comparisons: Exploiting Higher Order Interactions Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh
Supervising Unsupervised Learning Vikas Garg
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks Quan Zhang, Mingyuan Zhou
Adversarially Robust Generalization Requires More Data Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, Jeff Clune
Practical exact algorithm for trembling-hand equilibrium refinements in games Gabriele Farina, Nicola Gatti, Tuomas Sandholm
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen, Georgios Giannakis, Tao Sun, Wotao Yin
Scalable Robust Matrix Factorization with Nonconvex Loss Quanming Yao, James Kwok
Power-law efficient neural codes provide general link between perceptual bias and discriminability Michael Morais, Jonathan W. Pillow
Geometry-Aware Recurrent Neural Networks for Active Visual Recognition Ricson Cheng, Ziyan Wang, Katerina Fragkiadaki
Unsupervised Adversarial Invariance Ayush Jaiswal, Rex Yue Wu, Wael Abd-Almageed, Prem Natarajan
Content preserving text generation with attribute controls Lajanugen Logeswaran, Honglak Lee, Samy Bengio
Multi-armed Bandits with Compensation Siwei Wang, Longbo Huang
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, Salman Avestimehr
Learning in Games with Lossy Feedback Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye
Scalable methods for 8-bit training of neural networks Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li
Link Prediction Based on Graph Neural Networks Muhan Zhang, Yixin Chen
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task Dalin Guo, Angela J. Yu
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, Yinyu Ye
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan CHAN, Yanhui Geng
Contour location via entropy reduction leveraging multiple information sources Alexandre Marques, Remi Lam, Karen Willcox
Assessing Generative Models via Precision and Recall Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
A Convex Duality Framework for GANs Farzan Farnia, David Tse
Horizon-Independent Minimax Linear Regression Alan Malek, Peter L. Bartlett
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression Neha Gupta, Aaron Sidford
Experimental Design for Cost-Aware Learning of Causal Graphs Erik Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath
Task-Driven Convolutional Recurrent Models of the Visual System Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. Yamins
Meta-Reinforcement Learning of Structured Exploration Strategies Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, Sergey Levine
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation Tomoya Murata, Taiji Suzuki
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance Neal Jean, Sang Michael Xie, Stefano Ermon
Generalizing to Unseen Domains via Adversarial Data Augmentation Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Hyperbolic Neural Networks Octavian Ganea, Gary Becigneul, Thomas Hofmann
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
Learning Task Specifications from Demonstrations Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit Seshia
Learning a latent manifold of odor representations from neural responses in piriform cortex Anqi Wu, Stan Pashkovski, Sandeep R. Datta, Jonathan W. Pillow
Fully Understanding The Hashing Trick Lior Kamma, Casper B. Freksen, Kasper Green Larsen
Evolved Policy Gradients Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, Pieter Abbeel
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network Jeffrey Pennington, Pratik Worah
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra John T. Halloran, David M. Rocke
Differentially Private k-Means with Constant Multiplicative Error Uri Stemmer, Haim Kaplan
Policy Optimization via Importance Sampling
Estimating Learnability in the Sublinear Data Regime Weihao Kong, Gregory Valiant
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh
Community Exploration: From Offline Optimization to Online Learning Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui
A Dual Framework for Low-rank Tensor Completion Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu
Middle-Out Decoding Shikib Mehri, Leonid Sigal
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time Yi Xu, Jing Rong, Tianbao Yang
To Trust Or Not To Trust A Classifier Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee, Hangyeol Yu, Hongseok Yang
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization Zhize Li, Jian Li
Multimodal Generative Models for Scalable Weakly-Supervised Learning Mike Wu, Noah Goodman
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? Richard Zhang, Cedric Josz, Somayeh Sojoudi, Javad Lavaei
Occam’s razor is insufficient to infer the preferences of irrational agents Stuart Armstrong, Sören Mindermann
Manifold Structured Prediction Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, Lorenzo Rosasco
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity Laming Chen, Guoxin Zhang, Eric Zhou
Learning Others’ Intentional Models in Multi-Agent Settings Using Interactive POMDPs Yanlin Han, Piotr Gmytrasiewicz
Contextual Pricing for Lipschitz Buyers Jieming Mao, Renato Leme, Jon Schneider
Online Improper Learning with an Approximation Oracle Elad Hazan, Wei Hu, Yuanzhi Li, zhiyuan li
Bandit Learning in Concave N-Person Games Mario Bravo, David Leslie, Panayotis Mertikopoulos
On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
Unsupervised Video Object Segmentation for Deep Reinforcement Learning Vikash Goel, Jameson Weng, Pascal Poupart
Efficient inference for time-varying behavior during learning Nicholas G. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W. Pillow
Learning convex polytopes with margin Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
Critical initialisation for deep signal propagation in noisy rectifier neural networks Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper
Insights on representational similarity in neural networks with canonical correlation Ari Morcos, Maithra Raghu, Samy Bengio
Variational Inference with Tail-adaptive f-Divergence
Mental Sampling in Multimodal Representations Jianqiao Zhu, Adam Sanborn, Nick Chater
Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher
Learning to Multitask Yu Zhang, Ying Wei, Qiang Yang
Loss Functions for Multiset Prediction Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho
Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d+1)-partite graphs Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, Yan Liu
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces Liheng Zhang, Marzieh Edraki, Guo-Jun Qi
Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence Trong Dinh Thac Do, Longbing Cao
Masking: A New Perspective of Noisy Supervision Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, Masashi Sugiyama
On GANs and GMMs Eitan Richardson, Yair Weiss
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching Stepan Tulyakov, Anton Ivanov, François Fleuret
A Bayes-Sard Cubature Method Toni Karvonen, Chris J. Oates, Simo Sarkka
Dual Swap Disentangling Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao, Mingli Song
Diverse Ensemble Evolution: Curriculum Data-Model Marriage Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes
Binary Classification from Positive-Confidence Data Takashi Ishida, Gang Niu, Masashi Sugiyama
Deep Generative Models for Distribution-Preserving Lossy Compression Michael Tschannen, Eirikur Agustsson, Mario Lucic
Exact natural gradient in deep linear networks and its application to the nonlinear case Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin
Constructing Fast Network through Deconstruction of Convolution Yunho Jeon, Junmo Kim
Memory Replay GANs: Learning to Generate New Categories without Forgetting Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer, Bogdan Raducanu
The Convergence of Sparsified Gradient Methods Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cedric Renggli
Automating Bayesian optimization with Bayesian optimization Gustavo Malkomes, Roman Garnett
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning yunlong yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Multi-Task Zipping via Layer-wise Neuron Sharing Xiaoxi He, Zimu Zhou, Lothar Thiele
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo Oren Mangoubi, Nisheeth Vishnoi
Approximation algorithms for stochastic clustering David Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas Pensyl
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny
Learning to Infer Graphics Programs from Hand-Drawn Images Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum
Graphical Generative Adversarial Networks Chongxuan LI, Max Welling, Jun Zhu, Bo Zhang
Variational Learning on Aggregate Outputs with Gaussian Processes Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein
Information Constraints on Auto-Encoding Variational Bayes Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef
Recurrent Transformer Networks for Semantic Correspondence Seungryong Kim, Stephen Lin, SANG RYUL JEON, Dongbo Min, Kwanghoon Sohn
Online convex optimization for cumulative constraints Jianjun Yuan, Andrew Lamperski
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer David Madras, Toni Pitassi, Richard Zemel
Deep State Space Models for Unconditional Word Generation Florian Schmidt, Thomas Hofmann
ResNet with one-neuron hidden layers is a Universal Approximator Hongzhou Lin, Stefanie Jegelka
Transfer of Value Functions via Variational Methods Andrea Tirinzoni, Rafael Rodriguez Sanchez, Marcello Restelli
The Cluster Description Problem - Complexity Results, Formulations and Approximations Ian Davidson, Antoine Gourru, S Ravi
Sharp Bounds for Generalized Uniformity Testing Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Deep Neural Networks with Box Convolutions Egor Burkov, Victor Lempitsky
Learning towards Minimum Hyperspherical Energy Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song
LF-Net: Learning Local Features from Images Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming Bart van Merrienboer, Dan Moldovan, Alexander Wiltschko
Multi-domain Causal Structure Learning in Linear Systems AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle, Gilles Barthe, Marco Gaboardi
Exponentially Weighted Imitation Learning for Batched Historical Data Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu, Tong Zhang
Algebraic tests of general Gaussian latent tree models Dennis Leung, Mathias Drton
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models Minjia Zhang, Wenhan Wang, Xiaodong Liu, Jianfeng Gao, Yuxiong He
Deep Structured Prediction with Nonlinear Output Transformations Colin Graber, Ofer Meshi, Alexander Schwing
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
A no-regret generalization of hierarchical softmax to extreme multi-label classification Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski
Efficient Formal Safety Analysis of Neural Networks Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana
Bayesian Distributed Stochastic Gradient Descent Michael Teng, Frank Wood
Visualizing the Loss Landscape of Neural Nets Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
The Limits of Post-Selection Generalization Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, Jure Leskovec
On Controllable Sparse Alternatives to Softmax Anirban Laha, Saneem Ahmed Chemmengath, Priyanka Agrawal, Mitesh Khapra, Karthik Sankaranarayanan, Harish G. Ramaswamy
L4: Practical loss-based stepsize adaptation for deep learning Michal Rolinek, Georg Martius
Learning Latent Subspaces in Variational Autoencoders Jack Klys, Jake Snell, Richard Zemel
Turbo Learning for CaptionBot and DrawingBot Qiuyuan Huang, Pengchuan Zhang, Dapeng Wu, Lei Zhang
Learning to Teach with Dynamic Loss Functions Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation Edward Smith, Scott Fujimoto, David Meger
Size-Noise Tradeoffs in Generative Networks Bolton Bailey, Matus J. Telgarsky
Online Adaptive Methods, Universality and Acceleration Yehuda Kfir Levy, Alp Yurtsever, Volkan Cevher
Compact Generalized Non-local Network Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu
On the Local Hessian in Back-propagation Huishuai Zhang, Wei Chen, Tie-Yan Liu
The Everlasting Database: Statistical Validity at a Fair Price Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Proximal SCOPE for Distributed Sparse Learning Shenyi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li
On Coresets for Logistic Regression Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff
Neural Ordinary Differential Equations
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis Daan Wynen, Cordelia Schmid, Julien Mairal
Approximating Real-Time Recurrent Learning with Random Kronecker Factors Asier Mujika, Florian Meier, Angelika Steger
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes, Olga Ohrimenko
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression Sheng Chen, Arindam Banerjee
Incorporating Context into Language Encoding Models for fMRI Shailee Jain, Alexander Huth
CatBoost: unbiased boosting with categorical features Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
Query K-means Clustering and the Double Dixie Cup Problem I Chien, Chao Pan, Olgica Milenkovic
Training Neural Networks Using Features Replay Zhouyuan Huo, Bin Gu, Heng Huang
Modeling Dynamic Missingness of Implicit Feedback for Recommendation Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Representation Learning of Compositional Data Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
Model-based targeted dimensionality reduction for neuronal population data Mikio Aoi, Jonathan W. Pillow
On gradient regularizers for MMD GANs Michael Arbel, Dougal Sutherland, Mikołaj Bińkowski, Arthur Gretton
Heterogeneous Multi-output Gaussian Process Prediction Pablo Moreno-Muñoz, Antonio Artés, Mauricio Álvarez
Large-Scale Stochastic Sampling from the Probability Simplex Jack Baker, Paul Fearnhead, Emily Fox, Christopher Nemeth
Policy Regret in Repeated Games Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri
A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice Hendrik Fichtenberger, Dennis Rohde
Banach Wasserstein GAN Jonas Adler, Sebastian Lunz
Provable Gaussian Embedding with One Observation Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Princeton Zhaoran Wang
BRITS: Bidirectional Recurrent Imputation for Time Series Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, Yitan Li
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao
Extracting Relationships by Multi-Domain Matching Yitong Li, michael Murias, geraldine Dawson, David E. Carlson
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang
Generative Probabilistic Novelty Detection with Adversarial Autoencoders Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto
Diminishing Returns Shape Constraints for Interpretability and Regularization Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini
Scalable Hyperparameter Transfer Learning Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cedric Archambeau
Stochastic Nonparametric Event-Tensor Decomposition Shandian Zhe, Yishuai Du
Scaling Gaussian Process Regression with Derivatives David Eriksson, Kun Dong, Eric Lee, David Bindel, Andrew G. Wilson
Differentially Private Testing of Identity and Closeness of Discrete Distributions Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Bayesian Adversarial Learning Nanyang Ye, Zhanxing Zhu
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms Kishan Wimalawarne, Hiroshi Mamitsuka
Maximizing Induced Cardinality Under a Determinantal Point Process Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
Causal Inference with Noisy and Missing Covariates via Matrix Factorization Nathan Kallus, Xiaojie Mao, Madeleine Udell
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss Stephen Mussmann, Percy S. Liang
A Probabilistic U-Net for Segmentation of Ambiguous Images Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
Unorganized Malicious Attacks Detection Ming Pang, Wei Gao, Min Tao, Zhi-Hua Zhou
Causal Inference via Kernel Deviance Measures Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks Yingyezhe Jin, Wenrui Zhang, Peng Li
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
Efficient online algorithms for fast-rate regret bounds under sparsity Pierre Gaillard, Olivier Wintenberger
GILBO: One Metric to Measure Them All Alexander A. Alemi, Ian Fischer
Predictive Uncertainty Estimation via Prior Networks Andrey Malinin, Mark Gales
Dual Policy Iteration Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell
A probabilistic population code based on neural samples
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks Anirvan Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri Chklovskii
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee
Model-Agnostic Private Learning
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders Tengfei Ma, Jie Chen, Cao Xiao
Provably Correct Automatic Sub-Differentiation for Qualified Programs Sham M. Kakade, Jason D. Lee
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation Priyank Jaini, Pascal Poupart, Yaoliang Yu
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks Grant Rotskoff, Eric Vanden-Eijnden
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies Sungryull Sohn, Junhyuk Oh, Honglak Lee
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin
End-to-End Differentiable Physics for Learning and Control Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter
BRUNO: A Deep Recurrent Model for Exchangeable Data Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video Fabian Sinz, Alexander S. Ecker, Paul Fahey, Edgar Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Zachary Pitkow, Jacob Reimer, Andreas Tolias
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson
Distributed Multi-Player Bandits - a Game of Thrones Approach Ilai Bistritz, Amir Leshem
Efficient Loss-Based Decoding on Graphs for Extreme Classification Itay Evron, Edward Moroshko, Koby Crammer
Chaining Mutual Information and Tightening Generalization Bounds Amir Asadi, Emmanuel Abbe, Sergio Verdu
Implicit Probabilistic Integrators for ODEs Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
Learning Attentional Communication for Multi-Agent Cooperation Jiechuan Jiang, Zongqing Lu
Training Deep Models Faster with Robust, Approximate Importance Sampling Tyler B. Johnson, Carlos Guestrin
Bandit Learning with Implicit Feedback Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun
Unsupervised Text Style Transfer using Language Models as Discriminators Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick
Relational recurrent neural networks Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap
Streaming Kernel PCA with tilde{O}(sqrt{n}) Random Features Md Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Chang
Bayesian Model-Agnostic Meta-Learning Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
Disconnected Manifold Learning for Generative Adversarial Networks Mahyar Khayatkhoei, Maneesh K. Singh, Ahmed Elgammal
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James Glass
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem Victor-Emmanuel Brunel
Out-of-Distribution Detection using Multiple Semantic Label Representations Gabi Shalev, Yossi Adi, Joseph Keshet
Stochastic Chebyshev Gradient Descent for Spectral Optimization Insu Han, Haim Avron, Jinwoo Shin
Revisiting (epsilon, gamma, tau)-similarity learning for domain adaptation Sofiane Dhouib, Ievgen Redko
How to tell when a clustering is (approximately) correct using convex relaxations Marina Meila
Constant Regret, Generalized Mixability, and Mirror Descent Zakaria Mhammedi, Robert C. Williamson
A Bayesian Approach to Generative Adversarial Imitation Learning Wonseok Jeon, Seokin Seo, Kee-Eung Kim
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter
Constrained Cross-Entropy Method for Safe Reinforcement Learning Min Wen, Ufuk Topcu
Multi-Agent Generative Adversarial Imitation Learning Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
Adaptive Learning with Unknown Information Flows Yonatan Gur, Ahmadreza Momeni
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks Bryan Lim
Generative modeling for protein structures Namrata Anand, Possu Huang
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
Knowledge Distillation by On-the-Fly Native Ensemble xu lan, Xiatian Zhu, Shaogang Gong
Non-Adversarial Mapping with VAEs Yedid Hoshen
Generalisation in humans and deep neural networks Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann
Towards Text Generation with Adversarially Learned Neural Outlines Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Brendan McMahan
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G. Wilson
Diffusion Maps for Textual Network Embedding Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin
Simple, Distributed, and Accelerated Probabilistic Programming Dustin Tran, Matthew W. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul
VideoCapsuleNet: A Simplified Network for Action Detection Kevin Duarte, Yogesh Rawat, Mubarak Shah
Rectangular Bounding Process Xuhui Fan, Bin Li, Scott SIsson
Improved Algorithms for Collaborative PAC Learning Huy Nguyen, Lydia Zakynthinou
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding Nan Rosemary Ke, Anirudh Goyal ALIAS PARTH GOYAL, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio
Communication Compression for Decentralized Training Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu
Depth-Limited Solving for Imperfect-Information Games Noam Brown, Tuomas Sandholm, Brandon Amos
Training Deep Neural Networks with 8-bit Floating Point Numbers Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan
Scalar Posterior Sampling with Applications Georgios Theocharous, Zheng Wen, Yasin Abbasi, Nikos Vlassis
Understanding Batch Normalization Nils Bjorck, Carla P. Gomes, Bart Selman, Kilian Q. Weinberger
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision Rakshith R. Shetty, Mario Fritz, Bernt Schiele
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang
On Neuronal Capacity
Breaking the Activation Function Bottleneck through Adaptive Parameterization Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot
Learning Loop Invariants for Program Verification Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization Bruno Korbar, Du Tran, Lorenzo Torresani
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez Melis, Tommi Jaakkola
Deep State Space Models for Time Series Forecasting Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
Constrained Graph Variational Autoencoders for Molecule Design Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum
Neural Architecture Optimization Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu
Preference Based Adaptation for Learning Objectives Yao-Xiang Ding, Zhi-Hua Zhou
Distributed k-Clustering for Data with Heavy Noise Shi Li, Xiangyu Guo
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo HOLDEN LEE, Andrej Risteski, Rong Ge
A General Method for Amortizing Variational Filtering Joseph Marino, Milan Cvitkovic, Yisong Yue
A Reduction for Efficient LDA Topic Reconstruction Matteo Almanza, Flavio Chierichetti, Alessandro Panconesi, Andrea Vattani
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data Dominik Linzner, Heinz Koeppl
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes Thu H. Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yongliang Yang
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets RUI GAO, Liyan Xie, Yao Xie, Huan Xu
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks Zhihao Zheng, Pengyu Hong
Monte-Carlo Tree Search for Constrained POMDPs Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher Reale, Rebecca Russell, Louis Kim, peter chin
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu
Dirichlet belief networks for topic structure learning He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou
Stochastic Expectation Maximization with Variance Reduction Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions Wenruo Bai, William Stafford Noble, Jeff A. Bilmes
The challenge of realistic music generation: modelling raw audio at scale Sander Dieleman, Aaron van den Oord, Karen Simonyan
Spectral Signatures in Backdoor Attacks Brandon Tran, Jerry Li, Aleksander Madry
Reward learning from human preferences and demonstrations in Atari Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
Neural Arithmetic Logic Units Andrew Trask, Felix Hill, Scott E. Reed, Jack Rae, Chris Dyer, Phil Blunsom
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander Schwing
Improved Expressivity Through Dendritic Neural Networks Xundong Wu, Xiangwen Liu, wei li, qing wu
Efficient Anomaly Detection via Matrix Sketching Vatsal Sharan, Parikshit Gopalan, Udi Wieder
Learning to Specialize with Knowledge Distillation for Visual Question Answering Jonghwan Mun, Kimin Lee, Jinwoo Shin, Bohyung Han
A Lyapunov-based Approach to Safe Reinforcement Learning Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
Credit Assignment For Collective Multiagent RL With Global Rewards Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach
Does mitigating ML’s impact disparity require treatment disparity? Zachary Lipton, Julian McAuley, Alexandra Chouldechova
Proximal Graphical Event Models Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data Yuanzhi Li, Yingyu Liang
Hamiltonian Variational Auto-Encoder Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Modelling and unsupervised learning of symmetric deformable object categories James Thewlis, Hakan Bilen, Andrea Vedaldi
Graphical model inference: Sequential Monte Carlo meets deterministic approximations Fredrik Lindsten, Jouni Helske, Matti Vihola
Statistical mechanics of low-rank tensor decomposition Jonathan Kadmon, Surya Ganguli
Variational Bayesian Monte Carlo Luigi Acerbi
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
Efficient Online Portfolio with Logarithmic Regret Haipeng Luo, Chen-Yu Wei, Kai Zheng
Algorithms and Theory for Multiple-Source Adaptation Judy Hoffman, Mehryar Mohri, Ningshan Zhang
Online Reciprocal Recommendation with Theoretical Performance Guarantees Claudio Gentile, Nikos Parotsidis, Fabio Vitale
The promises and pitfalls of Stochastic Gradient Langevin Dynamics Nicolas Brosse, Alain Durmus, Eric Moulines
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective Lei Wu, Chao Ma, Weinan E
Differentiable MPC for End-to-end Planning and Control Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots, J. Zico Kolter
Bilevel learning of the Group Lasso structure Jordan Frecon, Saverio Salzo, Massimiliano Pontil
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
Information-theoretic Limits for Community Detection in Network Models Chuyang Ke, Jean Honorio
Learning Conditioned Graph Structures for Interpretable Visual Question Answering Will Norcliffe-Brown, Stathis Vafeias, Sarah Parisot
Distributionally Robust Graphical Models Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Transfer Learning with Neural AutoML Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu
On preserving non-discrimination when combining expert advice Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro
Learning to Play With Intrinsically-Motivated, Self-Aware Agents Nick Haber, Damian Mrowca, Stephanie Wang, Li F. Fei-Fei, Daniel L. Yamins
Scaling provable adversarial defenses Eric Wong, Frank Schmidt, Jan Hendrik Metzen, J. Zico Kolter
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu
Data-dependent PAC-Bayes priors via differential privacy Gintare Karolina Dziugaite, Daniel M. Roy
Deep Poisson gamma dynamical systems Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds Kry Lui, Gavin Weiguang Ding, Ruitong Huang, Robert McCann
Teaching Inverse Reinforcement Learners via Features and Demonstrations Luis Haug, Sebastian Tschiatschek, Adish Singla
Wasserstein Distributionally Robust Kalman Filtering Soroosh Shafieezadeh Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Mohajerin Esfahani
Generalisation of structural knowledge in the hippocampal-entorhinal system
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization Blake E. Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, Nati Srebro
Adversarial Regularizers in Inverse Problems Sebastian Lunz, Carola Schoenlieb, Ozan Öktem
Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn
Co-teaching: Robust training of deep neural networks with extremely noisy labels Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine
A convex program for bilinear inversion of sparse vectors Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi
Adversarial Multiple Source Domain Adaptation Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot-Guillarmod, Clement Hongler, Franck Gabriel
Contextual Stochastic Block Models Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks Jeffrey Chan, Valerio Perrone, Jeffrey Spence, Paul Jenkins, Sara Mathieson, Yun Song
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner
Randomized Prior Functions for Deep Reinforcement Learning Ian Osband, John Aslanides, Albin Cassirer
Compact Representation of Uncertainty in Clustering Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages Michelle Yuan, Benjamin Van Durme, Jordan L. Ying
Estimators for Multivariate Information Measures in General Probability Spaces Arman Rahimzamani, Himanshu Asnani, Pramod Viswanath, Sreeram Kannan
DeepPINK: reproducible feature selection in deep neural networks Yang Lu, Yingying Fan, Jinchi Lv, William Stafford Noble
HOUDINI: Lifelong Learning as Program Synthesis Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction Liang-Chieh Chen, Maxwell Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jon Shlens
Orthogonally Decoupled Variational Gaussian Processes Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth
Dendritic cortical microcircuits approximate the backpropagation algorithm
Learning Plannable Representations with Causal InfoGAN Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel
Uniform Convergence of Gradients for Non-Convex Learning and Optimization Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Automatic differentiation in ML: Where we are and where we should be going Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin
A Bayesian Nonparametric View on Count-Min Sketch Diana Cai, Michael Mitzenmacher, Ryan P. Adams
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang, Mert Sabuncu
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew G. Wilson
Flexible neural representation for physics prediction Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F. Fei-Fei, Josh Tenenbaum, Daniel L. Yamins
Legendre Decomposition for Tensors Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
Reinforcement Learning of Theorem Proving Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Miroslav Olšák
Data Amplification: A Unified and Competitive Approach to Property Estimation Yi HAO, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
Group Equivariant Capsule Networks Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
Stein Variational Gradient Descent as Moment Matching Qiang Liu, Dilin Wang
Differential Privacy for Growing Databases Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tantipongpipat
Exploration in Structured Reinforcement Learning
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh, Baba Vemuri
Balanced Policy Evaluation and Learning Nathan Kallus
Distributed Multitask Reinforcement Learning with Quadratic Convergence Rasul Tutunov, Dongho Kim, Haitham Bou Ammar
Improving Neural Program Synthesis with Inferred Execution Traces Richard Shin, Illia Polosukhin, Dawn Song
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian Ziebart
GLoMo: Unsupervised Learning of Transferable Relational Graphs Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan R. Salakhutdinov, Yann LeCun
Online Learning of Quantum States Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
Wavelet regression and additive models for irregularly spaced data Asad Haris, Ali Shojaie, Noah Simon
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes Rico Angell, Daniel R. Sheldon
A Structured Prediction Approach for Label Ranking Anna Korba, Alexandre Garcia, Florence d’Alché-Buc
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features Mojmir Mutny, Andreas Krause
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma
Reversible Recurrent Neural Networks Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse
SING: Symbol-to-Instrument Neural Generator Alexandre Defossez, Neil Zeghidour, Nicolas Usunier, Leon Bottou, Francis Bach
Learning Compressed Transforms with Low Displacement Rank Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
Iterative Value-Aware Model Learning Amir-massoud Farahmand
Invariant Representations without Adversarial Training Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias Abhinav Gupta, Adithyavairavan Murali, Dhiraj Prakashchand Gandhi, Lerrel Pinto
Learning Safe Policies with Expert Guidance Jessie Huang, Fa Wu, Doina Precup, Yang Cai
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian
Learning SMaLL Predictors Vikas Garg, Ofer Dekel, Lin Xiao
Phase Retrieval Under a Generative Prior
Quadrature-based features for kernel approximation Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
Reducing Network Agnostophobia Akshay Raj Dhamija, Manuel Günther, Terrance Boult
A Stein variational Newton method Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl
Watch Your Step: Learning Node Embeddings via Graph Attention Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi
Visual Reinforcement Learning with Imagined Goals Ashvin V. Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition Kuan Han, Haiguang Wen, Yizhen Zhang, Di Fu, Eugenio Culurciello, Zhongming Liu
PAC-Bayes bounds for stable algorithms with instance-dependent priors Omar Rivasplata, Csaba Szepesvari, John S. Shawe-Taylor, Emilio Parrado-Hernandez, Shiliang Sun
Beyond Grids: Learning Graph Representations for Visual Recognition Yin Li, Abhinav Gupta
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization Constantinos Daskalakis, Ioannis Panageas
Coordinate Descent with Bandit Sampling Farnood Salehi, Patrick Thiran, Elisa Celis
Deep Dynamical Modeling and Control of Unsteady Fluid Flows Jeremy Morton, Antony Jameson, Mykel J. Kochenderfer, Freddie Witherden
Confounding-Robust Policy Improvement Nathan Kallus, Angela Zhou
The Importance of Sampling inMeta-Reinforcement Learning Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, Pradeep K. Ravikumar
The Effect of Network Width on the Performance of Large-batch Training Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris Papailiopoulos, Paraschos Koutris
SNIPER: Efficient Multi-Scale Training Bharat Singh, Mahyar Najibi, Larry S. Davis
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
Hardware Conditioned Policies for Multi-Robot Transfer Learning Tao Chen, Adithyavairavan Murali, Abhinav Gupta
Co-regularized Alignment for Unsupervised Domain Adaptation Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman, Gregory Wornell
Statistical and Computational Trade-Offs in Kernel K-Means Daniele Calandriello, Lorenzo Rosasco
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap
Learning Attractor Dynamics for Generative Memory Yan Wu, Gregory Wayne, Karol Gregor, Timothy Lillicrap
The emergence of multiple retinal cell types through efficient coding of natural movies Samuel Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation Zi Yin, Vin Sachidananda, Balaji Prabhakar
Identification and Estimation of Causal Effects from Dependent Data Eli Sherman, Ilya Shpitser
Deepcode: Feedback Codes via Deep Learning Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Learning and Testing Causal Models with Interventions Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
PAC-Bayes Tree: weighted subtrees with guarantees Tin D. Nguyen, Samory Kpotufe
Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint Rajan Udwani
Sanity Checks for Saliency Maps Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn, Kelvin Xu, Sergey Levine
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom
Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent
Learning convex bounds for linear quadratic control policy synthesis Jack Umenberger, Thomas B. Schön
Neural Proximal Gradient Descent for Compressive Imaging Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John Pauly
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John Hopcroft
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
An intriguing failing of convolutional neural networks and the CoordConv solution Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski
Trading robust representations for sample complexity through self-supervised visual experience Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Invertibility of Convolutional Generative Networks from Partial Measurements Fangchang Ma, Ulas Ayaz, Sertac Karaman
Ex ante coordination and collusion in zero-sum multi-player extensive-form games Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization Hoi-To Wai, Zhuoran Yang, Princeton Zhaoran Wang, Mingyi Hong
Improving Online Algorithms via ML Predictions Manish Purohit, Zoya Svitkina, Ravi Kumar
Global Non-convex Optimization with Discretized Diffusions Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Theoretical guarantees for EM under misspecified Gaussian mixture models Raaz Dwivedi, nhật Hồ, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan
Coupled Variational Bayes via Optimization Embedding Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
Improving Explorability in Variational Inference with Annealed Variational Objectives Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville
Latent Alignment and Variational Attention Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander Rush
Towards Deep Conversational Recommendations Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
Unsupervised Depth Estimation, 3D Face Rotation and Replacement Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Chris Pal
Generalization Bounds for Uniformly Stable Algorithms Vitaly Feldman, Jan Vondrak
Deep Anomaly Detection Using Geometric Transformations Izhak Golan, Ran El-Yaniv
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport Theo Lacombe, Marco Cuturi, Steve OUDOT
Entropy Rate Estimation for Markov Chains with Large State Space Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu
Adaptive Methods for Nonconvex Optimization Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar
Object-Oriented Dynamics Predictor Guangxiang Zhu, Zhiao Huang, Chongjie Zhang
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation Matthew O’Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi
Reinforcement Learning for Solving the Vehicle Routing Problem MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder, Martin Takac
ATOMO: Communication-efficient Learning via Atomic Sparsification Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright
Dynamic Network Model from Partial Observations Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies Alessandro Achille, Tom Eccles, Loic Matthey, Chris Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins
Maximizing acquisition functions for Bayesian optimization James Wilson, Frank Hutter, Marc Deisenroth
On Markov Chain Gradient Descent Tao Sun, Yuejiao Sun, Wotao Yin
Variance-Reduced Stochastic Gradient Descent on Streaming Data Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip Gibbons, Srikanta Tirthapura
Online Robust Policy Learning in the Presence of Unknown Adversaries Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Uplift Modeling from Separate Labels Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama
Learning Invariances using the Marginal Likelihood Mark van der Wilk, Matthias Bauer, ST John, James Hensman
Non-delusional Q-learning and value-iteration Tyler Lu, Dale Schuurmans, Craig Boutilier
Using Large Ensembles of Control Variates for Variational Inference Tomas Geffner, Justin Domke
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization Yuanxiang Gao, Li Chen, Baochun Li
Learning to Reason with Third Order Tensor Products Imanol Schlag, Jürgen Schmidhuber
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams Tam Le, Makoto Yamada
Neural Voice Cloning with a Few Samples Sercan Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou
Blind Deconvolutional Phase Retrieval via Convex Programming Ali Ahmed, Alireza Aghasi, Paul Hand
Scalable Laplacian K-modes Imtiaz Ziko, Eric Granger, Ismail Ben Ayed
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Testing for Families of Distributions via the Fourier Transform Alistair Stewart, Ilias Diakonikolas, Clement Canonne
Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform Mitali Bafna, Jack Murtagh, Nikhil Vyas
Blockwise Parallel Decoding for Deep Autoregressive Models Mitchell Stern, Noam Shazeer, Jakob Uszkoreit
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch Osman Asif Malik, Stephen Becker
A Simple Cache Model for Image Recognition Emin Orhan
Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, Shubhendu Trivedi
Bayesian Nonparametric Spectral Estimation Felipe Tobar
A Spectral View of Adversarially Robust Features Shivam Garg, Vatsal Sharan, Brian Zhang, Gregory Valiant
Synaptic Strength For Convolutional Neural Network CHEN LIN, Zhao Zhong, Wu Wei, Junjie Yan
Human-in-the-Loop Interpretability Prior Isaac Lage, Andrew Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
Learning To Learn Around A Common Mean Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming Fei Wang, James Decker, Xilun Wu, Gregory Essertel, Tiark Rompf
Learning with SGD and Random Features Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco
Total stochastic gradient algorithms and applications in reinforcement learning Paavo Parmas
Glow: Generative Flow with Invertible 1x1 Convolutions Durk P. Kingma, Prafulla Dhariwal
Nonparametric Density Estimation under Adversarial Losses Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabas Poczos
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions Boris Muzellec, Marco Cuturi
Learning to Share and Hide Intentions using Information Regularization Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David J. Schwab
Predictive Approximate Bayesian Computation via Saddle Points Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Robustness of conditional GANs to noisy labels Kiran K. Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
Robust Learning of Fixed-Structure Bayesian Networks Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
Improving Simple Models with Confidence Profiles Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
PCA of high dimensional random walks with comparison to neural network training Joseph Antognini, Jascha Sohl-Dickstein
Learning to Solve SMT Formulas
Lifted Weighted Mini-Bucket Nicholas Gallo, Alexander T. Ihler
Learning and Inference in Hilbert Space with Quantum Graphical Models Siddarth Srinivasan, Carlton Downey, Byron Boots
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Iranmanesh, Nasser Nasrabadi
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution Dimitrios Diochnos, Saeed Mahloujifar, Mohammad Mahmoody
Gaussian Process Prior Variational Autoencoders Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data Maurice Weiler, Wouter Boomsma, Mario Geiger, Max Welling, Taco Cohen
Context-aware Synthesis and Placement of Object Instances Donghoon Lee, Ming-Yu Liu, Ming-Hsuan Yang, Sifei Liu, Jinwei Gu, Jan Kautz
Convex Elicitation of Continuous Properties Jessica Finocchiaro, Rafael Frongillo
Mesh-TensorFlow: Deep Learning for Supercomputers Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman
Learning Abstract Options Matthew Riemer, Miao Liu, Gerald Tesauro
Bounded-Loss Private Prediction Markets Rafael Frongillo, Bo Waggoner
Temporal alignment and latent Gaussian process factor inference in population spike trains Lea Duncker, Maneesh Sahani
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel
Discretely Relaxing Continuous Variables for tractable Variational Inference Trefor Evans, Prasanth Nair
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee
Deep Generative Models with Learnable Knowledge Constraints Zhiting Hu, Zichao Yang, Ruslan R. Salakhutdinov, LIANHUI Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing
The Sparse Manifold Transform Yubei Chen, Dylan Paiton, Bruno Olshausen
Bayesian Structure Learning by Recursive Bootstrap Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik
Complex Gated Recurrent Neural Networks Moritz Wolter, Angela Yao
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders Abubakar Abid, James Y. Zou
Streamlining Variational Inference for Constraint Satisfaction Problems Aditya Grover, Tudor Achim, Stefano Ermon
Fast deep reinforcement learning using online adjustments from the past Steven Hansen, Alexander Pritzel, Pablo Sprechmann, Andre Barreto, Charles Blundell
Improved Network Robustness with Adversary Critic Alexander Matyasko, Lap-Pui Chau
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint Shinji Ito, Daisuke Hatano, Sumita Hanna, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
Sketching Method for Large Scale Combinatorial Inference Wei Sun, Junwei Lu, Han Liu
Connecting Optimization and Regularization Paths Arun Suggala, Adarsh Prasad, Pradeep K. Ravikumar
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung
Understanding Regularized Spectral Clustering via Graph Conductance Yilin Zhang, Karl Rohe
Data-Driven Clustering via Parameterized Lloyd’s Families Maria-Florina F. Balcan, Travis Dick, Colin White
Learning Beam Search Policies via Imitation Learning Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon
Benefits of over-parameterization with EM Ji Xu, Daniel J. Hsu, Arian Maleki
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning Rui Luo, Jianhong Wang, Yaodong Yang, Jun WANG, Zhanxing Zhu
Robust Subspace Approximation in a Stream Roie Levin, Anish Prasad Sevekari, David Woodruff
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language Matthew D. Hoffman
DropBlock: A regularization method for convolutional networks Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier
With Friends Like These, Who Needs Adversaries? Saumya Jetley, Nicholas Lord, Philip Torr
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich
Joint Autoregressive and Hierarchical Priors for Learned Image Compression David Minnen, Johannes Ballé, George D. Toderici
Learning Temporal Point Processes via Reinforcement Learning Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song
Bias and Generalization in Deep Generative Models: An Empirical Study Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
Fast and Effective Robustness Certification Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli
Differentially Private Change-Point Detection Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations Tong Wang
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos Papadimitriou, Amin Saberi, Santosh Vempala
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep K. Ravikumar, Shou-De Lin
Semidefinite relaxations for certifying robustness to adversarial examples Aditi Raghunathan, Jacob Steinhardt, Percy S. Liang
Removing Hidden Confounding by Experimental Grounding Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar
Contrastive Learning from Pairwise Measurements Yi Chen, Zhuoran Yang, Yuchen Xie, Princeton Zhaoran Wang
Point process latent variable models of larval zebrafish behavior Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman
Computationally and statistically efficient learning of causal Bayes nets using path queries Kevin Bello, Jean Honorio
Sparse PCA from Sparse Linear Regression Guy Bresler, Sung Min Park, Madalina Persu
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices Don Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain
Transfer of Deep Reactive Policies for MDP Planning Aniket (Nick) Bajpai, Sankalp Garg, None
The Price of Fair PCA: One Extra dimension Samira Samadi, Uthaipon Tantipongpipat, Jamie H. Morgenstern, Mohit Singh, Santosh Vempala
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh

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