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
Training deep learning based denoisers without ground truth data Shakarim Soltanayev, Se Young Chun
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
But How Does It Work in Theory? Linear SVM with Random Features Yitong Sun, Anna Gilbert, Ambuj Tewari
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
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
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 Disenta