Comments for NIPS 2017 papers
Add your questions and comments to any of the papers being presented at the NIPS 2017 conference. Simply click on the paper that you want to comment.
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Monday, December 4th
Learning Active Learning from Data
Ksenia Konyushkova - Raphael Sznitman - Pascal Fua
Scalable Variational Inference for Dynamical Systems
Nico S Gorbach - Stefan Bauer - Joachim M Buhmann
Gradient Episodic Memory for Continual Learning
David Lopez-Paz - Marc'Aurelio Ranzato
Consistent Multitask Learning with Nonlinear Output Relations
Carlo Ciliberto - Alessandro Rudi - Lorenzo Rosasco - Massimiliano Pontil
Joint distribution optimal transportation for domain adaptation
Nicolas Courty - Rémi Flamary - Amaury Habrard - Alain Rakotomamonjy
Learning Multiple Tasks with Multilinear Relationship Networks
Mingsheng Long - ZHANGJIE CAO - Jianmin Wang - Philip S Yu
Matching neural paths: transfer from recognition to correspondence search
Nikolay Savinov - Lubor Ladicky - Marc Pollefeys
Do Deep Neural Networks Suffer from Crowding?
Anna Volokitin - Gemma Roig - Tomaso A Poggio
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
Maithra Raghu - Justin Gilmer - Jason Yosinski - Jascha Sohl-Dickstein
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles Ruizhongtai Qi - Li Yi - Hao Su - Leonidas J Guibas
Unsupervised Transformation Learning via Convex Relaxations
Tatsunori B Hashimoto - Percy Liang - John C Duchi
Deep Subspace Clustering Networks
Pan Ji - Tong Zhang - Hongdong Li - Mathieu Salzmann - Ian Reid
Learning Graph Representations with Embedding Propagation
Alberto Garcia Duran - Mathias Niepert
Unsupervised Sequence Classification using Sequential Output Statistics
Yu Liu - Jianshu Chen - Li Deng
Probabilistic Rule Realization and Selection
Haizi Yu - Tianxi Li - Lav Varshney
Trimmed Density Ratio Estimation
Song Liu - Akiko Takeda - Taiji Suzuki - Kenji Fukumizu
A Minimax Optimal Algorithm for Crowdsourcing
Thomas Bonald - Richard Combes
Adaptive Classification for Prediction Under a Budget
Feng Nan - Venkatesh Saligrama
Learning with Feature Evolvable Streams
Bo-Jian Hou - Lijun Zhang - Zhi-Hua Zhou
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Bikash Joshi - Massih-Reza Amini - Ioannis Partalas - Franck Iutzeler - Yury Maximov
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Matthias Hein - Maksym Andriushchenko
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Emmanouil Platanios - Hoifung Poon - Tom M Mitchell - Eric J Horvitz
Inferring Generative Model Structure with Static Analysis
Paroma Varma - Bryan He - Payal Bajaj - Nishith Khandwala - Imon Banerjee - Daniel Rubin - Christopher Ré
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Kristjan Greenewald - Seyoung Park - Shuheng Zhou - Alexander Giessing
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran - Trung Pham - Gustavo Carneiro - Lyle Palmer - Ian Reid
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer Bouchard - Alejandro Bujan - Farbod Roosta-Khorasani - Shashanka Ubaru - Mr. Prabhat - Antoine Snijders - Jian-Hua Mao - Edward Chang - Michael W Mahoney - Sharmodeep Bhattacharya
Deep Learning with Topological Signatures
Christoph Hofer - Roland Kwitt - Marc Niethammer - Andreas Uhl
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
Søren Dahlgaard - Mathias Knudsen - Mikkel Thorup
Kernel functions based on triplet comparisons
Matthäus Kleindessner - Ulrike von Luxburg
Multi-way Interacting Regression via Factorization Machines
Mikhail Yurochkin - XuanLong Nguyen - nikolaos Vasiloglou
Maximum Margin Interval Trees
Alexandre Drouin - Toby Hocking - Francois Laviolette
Kernel Feature Selection via Conditional Covariance Minimization
Jianbo Chen - Mitchell Stern - Martin J Wainwright - Michael Jordan
Improved Graph Laplacian via Geometric Self-Consistency
Dominique Joncas - Marina Meila - James McQueen
Predictive State Recurrent Neural Networks
Carlton Downey - Ahmed Hefny - Byron Boots - Geoffrey Gordon - Boyue Li
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
Chris Metzler - Ali Mousavi - Richard Baraniuk
Recursive Sampling for the Nystrom Method
Cameron Musco - Christopher Musco
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
Jeremiah Liu - Brent Coull
Testing and Learning on Distributions with Symmetric Noise Invariance
Law Ho Chung - Christopher Yau - Dino Sejdinovic
Max-Margin Invariant Features from Transformed Unlabelled Data
Dipan Pal - Ashwin Kannan - Gautam Arakalgud - Marios Savvides
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Zahra Ghodsi - Tianyu Gu - Siddharth Garg
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Hongyuan Mei - Jason Eisner
Inductive Representation Learning on Large Graphs
Will Hamilton - Zhitao Ying - Jure Leskovec
Federated Multi-Task Learning
Virginia Smith - Chao-Kai Chiang - Maziar Sanjabi - Ameet S Talwalkar
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Urs Köster - Tristan Webb - Xin Wang - Marcel Nassar - Arjun K Bansal - William Constable - Oguz Elibol - Stewart Hall - Luke Hornof - Amir Khosrowshahi - Carey Kloss - Ruby J Pai - Naveen Rao
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed M. Alaa - Mihaela van der Schaar
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt - Pieter-Jan Kindermans - Huziel Enoc Sauceda Felix - Stefan Chmiela - Alexandre Tkatchenko - Klaus-Robert Müller
Universal Style Transfer via Feature Transforms
Yijun Li - Chen Fang - Jimei Yang - Zhaowen Wang - Xin Lu - Ming-Hsuan Yang
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh - Jack Lanchantin - Arshdeep Sekhon - Yanjun Qi
Unbounded cache model for online language modeling with open vocabulary
Edouard Grave - Moustapha Cisse - Armand Joulin
Deconvolutional Paragraph Representation Learning
Yizhe Zhang - Dinghan Shen - Guoyin Wang - Zhe Gan - Ricardo Henao - Lawrence Carin
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Yonatan Belinkov - James Glass
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Jiasen Lu - Anitha Kannan - Jianwei Yang - Devi Parikh - Dhruv Batra
Teaching Machines to Describe Images via Natural Language Feedback
huan ling - Sanja Fidler
High-Order Attention Models for Visual Question Answering
Idan Schwartz - Alexander Schwing - Tamir Hazan
Visual Reference Resolution using Attention Memory for Visual Dialog
Paul Hongsuck Seo - Andreas Lehrmann - Bohyung Han - Leonid Sigal
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Alejandro Newell - Zhiao Huang - Jia Deng
Learning a Multi-View Stereo Machine
Abhishek Kar - Christian Häne - Jitendra Malik
Pose Guided Person Image Generation
Liqian Ma - Xu Jia - Qianru Sun - Bernt Schiele - Tinne Tuytelaars - Luc Van Gool
Working hard to know your neighbor's margins: Local descriptor learning loss
Anastasiia Mishchuk - Dmytro Mishkin - Filip Radenovic - Jiri Matas
Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu - Richard Zhang - Deepak Pathak - Trevor Darrell - Alexei Efros - Oliver Wang - Eli Shechtman
Deep Supervised Discrete Hashing
Qi Li - Zhenan Sun - Ran He - Tieniu Tan
Hash Embeddings for Efficient Word Representations
Dan Tito Svenstrup - Jonas Hansen - Ole Winther
A Regularized Framework for Sparse and Structured Neural Attention
Vlad Niculae - Mathieu Blondel
Attentional Pooling for Action Recognition
Rohit Girdhar - Deva Ramanan
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Caglar Gulcehre - Francis Dutil - Adam Trischler - Yoshua Bengio
Dilated Recurrent Neural Networks
Shiyu Chang - Yang Zhang - Wei Han - Mo Yu - Xiaoxiao Guo - Wei Tan - Xiaodong Cui - Michael Witbrock - Mark A Hasegawa-Johnson - Thomas Huang
Learning Hierarchical Information Flow with Recurrent Neural Modules
Danijar Hafner - Alexander Irpan - James Davidson - Nicolas Heess
Wasserstein Learning of Deep Generative Point Process Models
SHUAI XIAO - Mehrdad Farajtabar - Xiaojing Ye - Junchi Yan - Xiaokang Yang - Le Song - Hongyuan Zha
Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth - Aurelien Lucchi - Sebastian Nowozin - Thomas Hofmann
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov - Stefano Ermon
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Liwei Wang - Alexander Schwing - Svetlana Lazebnik
Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal ALIAS PARTH GOYAL - Alessandro Sordoni - Marc-Alexandre Côté - Nan Ke - Yoshua Bengio
One-Shot Imitation Learning
Yan Duan - Marcin Andrychowicz - Bradly Stadie - OpenAI Jonathan Ho - Jonas Schneider - Ilya Sutskever - Pieter Abbeel - Wojciech Zaremba
Fader Networks:Manipulating Images by Sliding Attributes
Guillaume Lample - Neil Zeghidour - Nicolas Usunier - Antoine Bordes - Ludovic DENOYER - Marc'Aurelio Ranzato
VAIN: Attentional Multi-agent Predictive Modeling
Yedid Hoshen
Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski - Yarin Gal
Prototypical Networks for Few-shot Learning
Jake Snell - Kevin Swersky - Richard Zemel
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou - Richard Zemel - Raquel Urtasun
The Reversible Residual Network: Backpropagation Without Storing Activations
Aidan N Gomez - Mengye Ren - Raquel Urtasun - Roger Grosse
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang - Erik Learned-Miller - Andrew McCallum
Decoupling 'when to update' from 'how to update'
Eran Malach - Shai Shalev-Shwartz
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Nanyang Ye - Zhanxing Zhu - Rafal Mantiuk
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Fan Yang - Zhilin Yang - William W Cohen
Saliency-based Sequential Image Attention with Multiset Prediction
Sean Welleck - Jialin Mao - Kyunghyun Cho - Zheng Zhang
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Arash Vahdat
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson - Fabian Mentzer - Michael Tschannen - Lukas Cavigelli - Radu Timofte - Luca Benini - Luc V Gool
Selective Classification for Deep Neural Networks
Yonatan Geifman - Ran El-Yaniv
Deep Lattice Networks and Partial Monotonic Functions
Seungil You - David Ding - Kevin Canini - Jan Pfeifer - Maya Gupta
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Xin Dong - Shangyu Chen - Sinno Pan
Bayesian Compression for Deep Learning
Christos Louizos - Karen Ullrich - Max Welling
Sobolev Training for Neural Networks
Wojciech M. Czarnecki - Simon Osindero - Max Jaderberg - Grzegorz Swirszcz - Razvan Pascanu
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov - Dmitry Molchanov - Arsenii Ashukha - Dmitry Vetrov
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Jeffrey Pennington - Samuel Schoenholz - Surya Ganguli
Robust Imitation of Diverse Behaviors
Ziyu Wang - Josh Merel - Scott Reed - Nando de Freitas - Gregory Wayne - Nicolas Heess
Question Asking as Program Generation
Anselm Rothe - Brenden Lake - Todd Gureckis
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann - Pedro J Goncalves - Giacomo Bassetto - Kaan Öcal - Marcel Nonnenmacher - Jakob H Macke
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Ingmar Kanitscheider - Ila Fiete
Neural system identification for large populations separating 'what' and 'where'
David Klindt - Alexander Ecker - Thomas Euler - Matthias Bethge
Stochastic Submodular Maximization: The Case of Coverage Functions
Mohammad Karimi - Mario Lucic - Hamed Hassani - Andreas Krause
Gradient Methods for Submodular Maximization
Hamed Hassani - Mahdi Soltanolkotabi - Amin Karbasi
Non-convex Finite-Sum Optimization Via SCSG Methods
Lihua Lei - Cheng Ju - Jianbo Chen - Michael Jordan
Polynomial time algorithms for dual volume sampling
Chengtao Li - Stefanie Jegelka - Suvrit Sra
Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Arturs Backurs - Piotr Indyk - Ludwig Schmidt
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Kinjal Basu - Ankan Saha - Shaunak Chatterjee
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
Ryan Tibshirani
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Ahmet Alacaoglu - Quoc Tran Dinh - Olivier Fercoq - Volkan Cevher
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari - Alejandro Ribeiro
Accelerated consensus via Min-Sum Splitting
Patrick Rebeschini - Sekhar C Tatikonda
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
Celestine Dünner - Thomas Parnell - Martin Jaggi
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
Constantinos Daskalakis - Nishanth Dikkala - Gautam Kamath
Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat A. Erdogdu - Yash Deshpande - Andrea Montanari
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Rebecca Morrison - Ricardo Baptista - Youssef Marzouk
Nonbacktracking Bounds on the Influence in Independent Cascade Models
Emmanuel Abbe - Sanjeev Kulkarni - Eun Jee Lee
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Alyson Fletcher - Mojtaba Sahraee-Ardakan - Sundeep Rangan - Philip Schniter
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Siddharth Narayanaswamy - T. Brooks Paige - Jan-Willem van de Meent - Alban Desmaison - Noah Goodman - Pushmeet Kohli - Frank Wood - Philip Torr
Gauging Variational Inference
Sung-Soo Ahn - Michael Chertkov - Jinwoo Shin
Variational Inference via χ Upper Bound Minimization
Adji Bousso Dieng - Dustin Tran - Rajesh Ranganath - John Paisley - David Blei
Bayesian Dyadic Trees and Histograms for Regression
Stéphanie van der Pas - Veronika Rockova
Differentially private Bayesian learning on distributed data
Mikko Heikkilä - Eemil Lagerspetz - Samuel Kaski - Kana Shimizu - Sasu Tarkoma - Antti Honkela
Model-Powered Conditional Independence Test
Rajat Sen - Ananda Theertha Suresh - Karthikeyan Shanmugam - Alexandros Dimakis - Sanjay Shakkottai
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris Oates - Steven Niederer - Angela Lee - François-Xavier Briol - Mark Girolami
Expectation Propagation for t-Exponential Family Using q-Algebra
Futoshi Futami - Issei Sato - Masashi Sugiyama
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su - xuejun Liao - Lawrence Carin
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
Yunzhu Li - Jiaming Song - Stefano Ermon
Hybrid Reward Architecture for Reinforcement Learning
Harm Van Seijen - Mehdi Fatemi - Romain Laroche - Joshua Romoff - Tavian Barnes - Jeffrey Tsang
Shallow Updates for Deep Reinforcement Learning
Nir Levine - Tom Zahavy - Daniel J Mankowitz - Aviv Tamar - Shie Mannor
Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran - Kendall Lowrey - Emanuel Todorov - Sham Kakade
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Shixiang Gu - Tim Lillicrap - Richard E Turner - Zoubin Ghahramani - Bernhard Schölkopf - Sergey Levine
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu - Buser Say - Scott Sanner
Task-based End-to-end Model Learning in Stochastic Optimization
Priya Donti - J. Zico Kolter - Brandon Amos
Value Prediction Network
Junhyuk Oh - Satinder Singh - Honglak Lee
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu - Hongming Pu - Feicheng Wang - Zhiqiang Hu - Liwei Wang
SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Ben London
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Songbai Yan - Chicheng Zhang
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich - Sivan Sabato - Roi Weiss
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Asish Ghoshal - Jean Honorio
From which world is your graph?
Cheng Li - Felix MF Wong - Zhenming Liu - Varun Kanade
On clustering network-valued data
Soumendu Sundar Mukherjee - Purnamrita Sarkar - Lizhen Lin
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon Du - Yining Wang - Aarti Singh
Discovering Potential Correlations via Hypercontractivity
Hyeji Kim - Weihao Gao - Sreeram Kannan - Sewoong Oh - Pramod Viswanath
Phase Transitions in the Pooled Data Problem
Jonathan Scarlett - Volkan Cevher
Query Complexity of Clustering with Side Information
Arya Mazumdar - Barna Saha
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Lixin Fan
Tuesday, December 5th
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control
Fanny Yang - Aaditya Ramdas - Kevin Jamieson - Martin Wainwright
Monte-Carlo Tree Search by Best Arm Identification
Emilie Kaufmann - Wouter Koolen
Minimal Exploration in Structured Stochastic Bandits
Richard Combes - Stefan Magureanu - Alexandre Proutiere
Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai
Elementary Symmetric Polynomials for Optimal Experimental Design
Zelda E. Mariet - Suvrit Sra
Learning Linear Dynamical Systems via Spectral Filtering
Elad Hazan - Karan Singh - Cyril Zhang
Online multiclass boosting
Young Hun Jung - Jack Goetz - Ambuj Tewari
Universal consistency and minimax rates for online Mondrian Forests
Jaouad Mourtada - Stéphane Gaïffas - Erwan Scornet
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen - Harri Valpola
Learning from Complementary Labels
Takashi Ishida - Gang Niu - Weihua Hu - Masashi Sugiyama
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo - Gang Niu - Marthinus C du Plessis - Masashi Sugiyama
On Optimal Generalizability in Parametric Learning
Ahmad Beirami - Meisam Razaviyayn - Shahin Shahrampour - Vahid Tarokh
Multi-Objective Non-parametric Sequential Prediction
Guy Uziel - Ran El-Yaniv
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Joel A Tropp - Alp Yurtsever - Madeleine Udell - Volkan Cevher
Practical Data-Dependent Metric Compression with Provable Guarantees
Piotr Indyk - Ilya Razenshteyn - Tal Wagner
K-Medoids For K-Means Seeding
James Newling - François Fleuret
Inhomogeneous Hypergraph Clustering with Applications
Pan Li - Olgica Milenkovic
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg - Su-In Lee
Unbiased estimates for linear regression via volume sampling
Michal Derezinski - Manfred Warmuth
Generalized Linear Model Regression under Distance-to-set Penalties
Jason Xu - Eric Chi - Kenneth Lange
Learning Overcomplete HMMs
Vatsal Sharan - Sham Kakade - Percy Liang - Gregory Valiant
Optimal Shrinkage of Singular Values Under Random Data Contamination
Danny Barash - Matan Gavish
Learning Low-Dimensional Metrics
Blake Mason - Lalit Jain - Robert Nowak
Alternating minimization for dictionary learning with random initialization
Niladri Chatterji - Peter Bartlett
Minimax Estimation of Bandable Precision Matrices
Addison Hu - Sahand Negahban
Estimation of the covariance structure of heavy-tailed distributions
Xiaohan Wei - Stanislav Minsker
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi - Yoshinobu Kawahara - Takehisa Yairi
Stochastic Approximation for Canonical Correlation Analysis
Raman Arora - Teodor Vanislavov Marinov - Poorya Mianjy - Nati Srebro
Diving into the shallows: a computational perspective on large-scale shallow learning
SIYUAN MA - Mikhail Belkin
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof M Choromanski - Mark Rowland - Adrian Weller
Generalization Properties of Learning with Random Features
Alessandro Rudi - Lorenzo Rosasco
Gaussian Quadrature for Kernel Features
Tri Dao - Christopher M De Sa - Christopher Ré
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum - Wenkai Xu - Zoltan Szabo - Kenji Fukumizu - Arthur Gretton
Learning Populations of Parameters
Kevin Tian - Weihao Kong - Gregory Valiant
Improved Dynamic Regret for Non-degenerate Functions
Lijun Zhang - Tianbao Yang - Jinfeng Yi - Jing Rong - Zhi-Hua Zhou
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Quentin Berthet - Vianney Perchet
Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt - Pang Wei W Koh - Percy Liang
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Masaaki Imaizumi - Takanori Maehara - Kohei Hayashi
Sparse convolutional coding for neuronal assembly detection
Sven Peter - Elke Kirschbaum - Martin Both - Lee Campbell - Brandon Harvey - Conor Heins - Daniel Durstewitz - Ferran Diego - Fred Hamprecht
Learned in Translation: Contextualized Word Vectors
Bryan McCann - James Bradbury - Caiming Xiong - Richard Socher
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Andrew Gibiansky - Sercan Arik - Gregory Diamos - John Miller - Kainan Peng - Wei Ping - Jonathan Raiman - Yanqi Zhou
Modulating early visual processing by language
Harm de Vries - Florian Strub - Jeremie Mary - Hugo Larochelle - Olivier Pietquin - Aaron C Courville
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli - Matthias Zwicker - Paolo Favaro - Meiguang Jin
Pixels to Graphs by Associative Embedding
Alejandro Newell - Jia Deng
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu - Yifan Wang - Tianfan Xue - Xingyuan Sun - Bill Freeman - Josh Tenenbaum
Self-Supervised Intrinsic Image Decomposition
Michael Janner - Jiajun Wu - Tejas Kulkarni - Ilker Yildirim - Josh Tenenbaum
Unsupervised learning of object frames by dense equivariant image labelling
James Thewlis - Hakan Bilen - Andrea Vedaldi
One-Sided Unsupervised Domain Mapping
Sagie Benaim - Lior Wolf
Contrastive Learning for Image Captioning
Bo Dai - Dahua Lin
Dynamic Routing Between Capsules
Sara Sabour - Nicholas Frosst - Geoffrey E Hinton
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall - Yarin Gal
Label Distribution Learning Forests
Wei Shen - KAI ZHAO - Yilu Guo - Alan Yuille
Training Quantized Nets: A Deeper Understanding
Hao Li - Soham De - Zheng Xu - Christoph Studer - Hanan Samet - Tom Goldstein
An inner-loop free solution to inverse problems using deep neural networks
Kai Fan - Qi Wei - Lawrence Carin - Katherine A Heller
Towards Accurate Binary Convolutional Neural Network
Xiaofan Lin - Cong Zhao - Wei Pan
Structured Embedding Models for Grouped Data
Maja Rudolph - Francisco Ruiz - Susan Athey - David Blei
Poincaré Embeddings for Learning Hierarchical Representations
Maximillian Nickel - Douwe Kiela
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Zhen He - Shaobing Gao - Liang Xiao - Daxue Liu - Hangen He - David Barber
Fast-Slow Recurrent Neural Networks
Asier Mujika - Florian Meier - Angelika Steger
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Nan Ding - Radu Soricut
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Xingjian Shi - Zhihan Gao - Leonard Lausen - Hao Wang - Dit-Yan Yeung - Wai-kin Wong - Wang-chun WOO
Recurrent Ladder Networks
Isabeau Prémont-Schwarz - Alexander Ilin - Tele Hao - Antti Rasmus - Rinu Boney - Harri Valpola
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman - Nicholas Rhinehart - Wen Sun - Lerrel Pinto - Martial Hebert - Byron Boots - Kris Kitani - J. Bagnell
QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus - David Hsu - Wee Sun Lee
Filtering Variational Objectives
Chris Maddison - John Lawson - George Tucker - Nicolas Heess - Mohammad Norouzi - Andriy Mnih - Arnaud Doucet - Yee Teh
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu - Yu Zhang - James Glass
Neural Discrete Representation Learning
Aaron van den Oord - Oriol Vinyals - koray kavukcuoglu
Variational Memory Addressing in Generative Models
Jörg Bornschein - Andriy Mnih - Daniel Zoran - Danilo Jimenez Rezende
Cortical microcircuits as gated-recurrent neural networks
Rui Costa - Yannis Assael - Brendan Shillingford - Nando de Freitas - TIm Vogels
Continual Learning with Deep Generative Replay
Hanul Shin - Jung Kwon Lee - Jaehong Kim - Jiwon Kim
Hierarchical Attentive Recurrent Tracking
Adam Kosiorek - Alex Bewley - Ingmar Posner
VAE Learning via Stein Variational Gradient Descent
Yuchen Pu - Zhe Gan - Ricardo Henao - Chunyuan Li - Shaobo Han - Lawrence Carin
Learning to Inpaint for Image Compression
Mohammad Haris Baig - Vladlen Koltun - Lorenzo Torresani
Visual Interaction Networks: Learning a Physics Simulator from Video
Nicholas Watters - Daniel Zoran - Theophane Weber - Peter Battaglia - Razvan Pascanu - Andrea Tacchetti
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Fei Xia - Martin J Zhang - James Zou - David Tse
Eigen-Distortions of Hierarchical Representations
Alexander Berardino - Valero Laparra - Johannes Ballé - Eero Simoncelli
On-the-fly Operation Batching in Dynamic Computation Graphs
Graham Neubig - Yoav Goldberg - Chris Dyer
Learning Affinity via Spatial Propagation Networks
Sifei Liu - Shalini De Mello - Jinwei Gu - Guangyu Zhong - Ming-Hsuan Yang - Jan Kautz
Few-Shot Adversarial Domain Adaptation
Saeid Motiian - Quinn Jones - Seyed Iranmanesh - Gianfranco Doretto
Deep Hyperspherical Learning
Weiyang Liu - Yan-Ming Zhang - Xingguo Li - Zhiding Yu - Bo Dai - Tuo Zhao - Le Song
Riemannian approach to batch normalization
Minhyung Cho - Jaehyung Lee
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona - Tatiana Tommasi
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Ziming Zhang - Matthew Brand
Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang - Aditya Balu - Chinmay Hegde - Soumik Sarkar
How regularization affects the critical points in linear networks
Amirhossein Taghvaei - Jin W Kim - Prashant Mehta
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin - Connor Coley - Regina Barzilay - Tommi Jaakkola
Predicting Scene Parsing and Motion Dynamics in the Future
Xiaojie Jin - Huaxin Xiao - Xiaohui Shen - Jimei Yang - Zhe Lin - Yunpeng Chen - Zequn Jie - Jiashi Feng - Shuicheng Yan
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
Moustapha Cisse - Yossi Adi - Natalia Neverova - Joseph Keshet
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti - Michael Bronstein - Xavier Bresson
Compression-aware Training of Deep Networks
Jose Alvarez - Mathieu Salzmann
Exploring Generalization in Deep Learning
Behnam Neyshabur - Srinadh Bhojanapalli - David Mcallester - Nati Srebro
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Hyeonwoo Noh - Tackgeun You - Jonghwan Mun - Bohyung Han
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher - Srinivas C Turaga - Jakob H Macke
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Luigi Acerbi - Wei Ji
An Error Detection and Correction Framework for Connectomics
Jonathan Zung - Ignacio Tartavull - Kisuk Lee - H. Sebastian Seung
GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni - Max Hinne - Marcel Van Gerven - Eric Maris
Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch - Julien Mairal - Danilo Bzdok - Bertrand Thirion - Gael Varoquaux
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Mainak Jas - Tom Dupré la Tour - Umut Simsekli - Alexandre Gramfort
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Ethan Elenberg - Alexandros Dimakis - Moran Feldman - Amin Karbasi
Decomposable Submodular Function Minimization: Discrete and Continuous
Alina Ene - Huy Nguyen - László A. Végh
Robust Optimization for Non-Convex Objectives
Robert S Chen - Brendan Lucier - Yaron Singer - Vasilis Syrgkanis
On the Optimization Landscape of Tensor Decompositions
Rong Ge - Tengyu Ma
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon Du - Chi Jin - Jason D Lee - Michael Jordan - Aarti Singh - Barnabas Poczos
Implicit Regularization in Matrix Factorization
Suriya Gunasekar - Blake Woodworth - Srinadh Bhojanapalli - Behnam Neyshabur - Nati Srebro
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Jason Altschuler - Jonathan Weed - Philippe Rigollet
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello - Michael Tschannen - Gunnar Raetsch - Martin Jaggi
Adaptive Accelerated Gradient Converging Method under Holderian Error Bound Condition
Mingrui Liu - Tianbao Yang
Geometric Descent Method for Convex Composite Minimization
Shixiang Chen - Shiqian Ma - Wei Liu
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Cong Fang - Feng Cheng - Zhouchen Lin
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Tomoya Murata - Taiji Suzuki
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
Yossi Arjevani
Nonlinear Acceleration of Stochastic Algorithms
Damien Scieur - Francis Bach - Alexandre d'Aspremont
Acceleration and Averaging in Stochastic Descent Dynamics
Walid Krichene - Peter Bartlett
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations
Pan Xu - Jian Ma - Quanquan Gu
Reducing Reparameterization Gradient Variance
Andrew Miller - Nick Foti - Alexander D'Amour - Ryan Adams
Robust Conditional Probabilities
Yoav Wald - Amir Globerson
Stein Variational Gradient Descent as Gradient Flow
Qiang Liu
Parallel Streaming Wasserstein Barycenters
Matthew Staib - Sebastian Claici - Justin M Solomon - Stefanie Jegelka
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco Cusumano-Towner - Vikash K Mansinghka
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Iku Ohama - Issei Sato - Takuya Kida - Hiroki Arimura
A-NICE-MC: Adversarial Training for MCMC
Jiaming Song - Shengjia Zhao - Stefano Ermon
Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis - Tom Nicholson - Marc Deisenroth - James Hensman
Streaming Sparse Gaussian Process Approximations
Thang D Bui - Cuong Nguyen - Richard E Turner
Variational Inference for Gaussian Process Models with Linear Complexity
Ching-An Cheng - Byron Boots
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Zhenwen Dai - Mauricio A. Álvarez - Neil Lawrence
Non-Stationary Spectral Kernels
Sami Remes - Markus Heinonen - Samuel Kaski
Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong - David Eriksson - Hannes Nickisch - David Bindel - Andrew Wilson
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Gabriel Parra - Felipe Tobar
Linearly constrained Gaussian processes
Carl Jidling - Niklas Wahlström - Adrian Wills - Thomas B Schön
Hindsight Experience Replay
Marcin Andrychowicz - Dwight Crow - Alex Ray - Jonas Schneider - Rachel Fong - Peter Welinder - Bob McGrew - Josh Tobin - OpenAI Pieter Abbeel - Wojciech Zaremba
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp - Matteo Turchetta - Angela Schoellig - Andreas Krause
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
Rowan McAllister - Carl Edward Rasmussen
Linear regression without correspondence
Daniel Hsu - Kevin Shi - Xiaorui Sun
On the Complexity of Learning Neural Networks
Le Song - Santosh Vempala - John Wilmes - Bo Xie
Near Optimal Sketching of Low-Rank Tensor Regression
Xingguo Li - Jarvis Haupt - David Woodruff
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Cameron Musco - David Woodruff
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Sheng Chen - Arindam Banerjee
Adaptive Clustering through Semidefinite Programming
Martin Royer
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
Surbhi Goel - Adam Klivans
Learning with Average Top-k Loss
Yanbo Fan - Siwei Lyu - Yiming Ying - Baogang Hu
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi - Afshin Abdi - Nam Nguyen - Justin Romberg
A graph-theoretic approach to multitasking
Noga Alon - Daniel Reichman - Igor Shinkar - Tal Wagner - Sebastian Musslick - Jonathan D Cohen - Tom Griffiths - Biswadip dey - Kayhan Ozcimder
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu - Maxim Raginsky
Independence clustering (without a matrix)
Daniil Ryabko
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
Qian Yu - Mohammad Maddah-Ali - Salman Avestimehr
Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao - Sreeram Kannan - Sewoong Oh - Pramod Viswanath
Statistical Cost Sharing
Eric Balkanski - Umar Syed - Sergei Vassilvitskii
A Sample Complexity Measure with Applications to Learning Optimal Auctions
Vasilis Syrgkanis
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos
Gerasimos Palaiopanos - Ioannis Panageas - Georgios Piliouras
Safe and Nested Subgame Solving for Imperfect-Information Games
Noam Brown - Tuomas Sandholm
Wednesday, December 6th
Deep Reinforcement Learning from Human Preferences
Paul F Christiano - Jan Leike - Tom Brown - Miljan Martic - Shane Legg - Dario Amodei
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Karol Hausman - Yevgen Chebotar - Stefan Schaal - Gaurav Sukhatme - Joseph J Lim
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu - John Co-Reyes - Sergey Levine
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang - Rein Houthooft - Davis Foote - Adam Stooke - OpenAI Xi Chen - Yan Duan - John Schulman - Filip DeTurck - Pieter Abbeel
Thinking Fast and Slow with Deep Learning and Tree Search
Thomas Anthony - Zheng Tian - David Barber
Active Exploration for Learning Symbolic Representations
Garrett Andersen - George Konidaris
Successor Features for Transfer in Reinforcement Learning
Andre Barreto - Will Dabney - Remi Munos - Jonathan Hunt - Tom Schaul - David Silver - Hado van Hasselt
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum - Mohammad Norouzi - Kelvin Xu - Dale Schuurmans
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Zhaohan Guo - Philip S. Thomas - Emma Brunskill
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang - Mukul Gagrani - Ashutosh Nayyar - Rahul Jain
Reinforcement Learning under Model Mismatch
Aurko Roy - Huan Xu - Sebastian Pokutta
Ensemble Sampling
Xiuyuan Lu - Benjamin Van Roy
Action Centered Contextual Bandits
Kristjan Greenewald - Ambuj Tewari - Susan Murphy - Predag Klasnja
Conservative Contextual Linear Bandits
Abbas Kazerouni - Mohammad Ghavamzadeh - Yasin Abbasi - Benjamin Van Roy
Rotting Bandits
Nir Levine - Koby Crammer - Shie Mannor
Multi-Task Learning for Contextual Bandits
Aniket Anand Deshmukh - Urun Dogan - Clay Scott
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi - Claudio Gentile - Gergely Neu - Gabor Lugosi
Improving the Expected Improvement Algorithm
Chao Qin - Diego Klabjan - Daniel Russo
A KL-LUCB algorithm for Large-Scale Crowdsourcing
Ervin Tanczos - Robert Nowak - Bob Mankoff
Scalable Generalized Linear Bandits: Online Computation and Hashing
Kwang-Sung Jun - Aniruddha Bhargava - Robert Nowak - Rebecca Willett
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Zheng Wen - Branislav Kveton - Michal Valko - Sharan Vaswani
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Tor Lattimore
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Sanjiban Choudhury - Shervin Javdani - Siddhartha Srinivasa - Sebastian Scherer
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor W Killian - Samuel Daulton - Finale Doshi-Velez - George Konidaris
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee - Jin-Hwa Kim - Jaehyun Jun - Jung-Woo Ha - Byoung-Tak Zhang
Hypothesis Transfer Learning via Transformation Functions
Simon Du - Jayanth Koushik - Aarti Singh - Barnabas Poczos
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi - Hakan Bilen - Andrea Vedaldi
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Linus Hamilton - Frederic Koehler - Ankur Moitra
From Bayesian Sparsity to Gated Recurrent Nets
Hao He - Bo Xin - Satoshi Ikehata - David Wipf
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
Kfir Levy
Nonparametric Online Regression while Learning the Metric
Ilja Kuzborskij - Nicolò Cesa-Bianchi
Online Convex Optimization with Stochastic Constraints
Hao Yu - Michael Neely - Xiaohan Wei
Efficient Online Linear Optimization with Approximation Algorithms
Dan Garber
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
M. Sevi Baltaoglu - Lang Tong - Qing Zhao
Online Prediction with Selfish Experts
Tim Roughgarden - Okke Schrijvers
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Qinshi Wang - Wei Chen
A General Framework for Robust Interactive Learning
Ehsan Emamjomeh-Zadeh - David Kempe
Practical Locally Private Heavy Hitters
Raef Bassily - kobbi nissim - Uri Stemmer - Abhradeep Guha Thakurta
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Katrina Ligett - Seth Neel - Aaron Roth - Bo Waggoner - Steven Wu
Renyi Differential Privacy Mechanisms for Posterior Sampling
Joseph Geumlek - Shuang Song - Kamalika Chaudhuri
Generating steganographic images via adversarial training
Jamie Hayes - George Danezis
On Fairness and Calibration
Geoff Pleiss - Manish Raghavan - Felix Wu - Jon Kleinberg - Kilian Weinberger
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus - Mateo Rojas Carulla - Giambattista Parascandolo - Moritz Hardt - Dominik Janzing - Bernhard Schölkopf
Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon - Dennis Wei - Bhanukiran Vinzamuri - Karthikeyan Natesan Ramamurthy - Kush R Varshney
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar - Isabel Valera - Manuel Rodriguez - Krishna Gummadi - Adrian Weller
Beyond Parity: Fairness Objectives for Collaborative Filtering
Sirui Yao - Bert Huang
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Hongteng Xu - Hongyuan Zha
Off-policy evaluation for slate recommendation
Adith Swaminathan - Akshay Krishnamurthy - Alekh Agarwal - Miro Dudik - John Langford - Damien Jose - Imed Zitouni
A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Pérolat - Joel Leibo - Vinicius Zambaldi - Charles Beattie - Karl Tuyls - Thore Graepel
Balancing information exposure in social networks
Kiran Garimella - Aristides Gionis - Nikos Parotsidis - Nikolaj Tatti
Scalable Demand-Aware Recommendation
Jinfeng Yi - Cho-Jui Hsieh - Kush R Varshney - Lijun Zhang - Yao Li
A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu - Cho-Jui Hsieh - Qi Lei - Inderjit S Dhillon
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang - Yihe Tang - Jian Wang - Jia Deng
Style Transfer from Non-Parallel Text by Cross-Alignment
Tianxiao Shen - Tao Lei - Regina Barzilay - Tommi Jaakkola
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov - Ivan Titov
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Yuandong Tian - Qucheng Gong - Wenling Shang - Yuxin Wu - C. Lawrence Zitnick
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Evan Racah - Christopher Beckham - Tegan Maharaj - Samira Ebrahimi Kahou - Mr. Prabhat - Chris Pal
Approximation and Convergence Properties of Generative Adversarial Learning
Shuang Liu - Olivier Bousquet - Kamalika Chaudhuri
Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan - J. Zico Kolter
f-GANs in an Information Geometric Nutshell
Richard Nock - Zac Cranko - Aditya K Menon - Lizhen Qu - Robert C Williamson
The Numerics of GANs
Lars Mescheder - Sebastian Nowozin - Andreas Geiger
Dualing GANs
Yujia Li - Alexander Schwing - Kuan-Chieh Wang - Richard Zemel
Fisher GAN
Youssef Mroueh - Tom Sercu
Learning to Pivot with Adversarial Networks
Gilles Louppe - Michael Kagan - Kyle Cranmer
Improved Training of Wasserstein GANs
Ishaan Gulrajani - Faruk Ahmed - Martin Arjovsky - Vincent Dumoulin - Aaron C Courville
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li - Wei-Cheng Chang - Yu Cheng - Yiming Yang - Barnabas Poczos
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel - Hubert Ramsauer - Thomas Unterthiner - Bernhard Nessler - Sepp Hochreiter
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava - Lazar Valkoz - Chris Russell - Michael U. Gutmann - Charles Sutton
Good Semi-supervised Learning That Requires a Bad GAN
Zihang Dai - Zhilin Yang - Fan Yang - William W Cohen - Ruslan Salakhutdinov
Dual Discriminator Generative Adversarial Nets
Tu Nguyen - Trung Le - Hung Vu - Dinh Phung
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li - Hao Liu - Changyou Chen - Yuchen Pu - Liqun Chen - Ricardo Henao - Lawrence Carin
Triple Generative Adversarial Nets
Chongxuan LI - Taufik Xu - Jun Zhu - Bo Zhang
Triangle Generative Adversarial Networks
Zhe Gan - Liqun Chen - Weiyao Wang - Yuchen Pu - Yizhe Zhang - Hao Liu - Chunyuan Li - Lawrence Carin
Structured Generative Adversarial Networks
Zhijie Deng - Hao Zhang - Xiaodan Liang - Luona Yang - Shizhen Xu - Jun Zhu - Eric P Xing
PixelGAN Autoencoders
Alireza Makhzani - Brendan J Frey
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander Ratner - Henry Ehrenberg - Zeshan Hussain - Jared Dunnmon - Christopher Ré
Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu - Thomas Breuel - Jan Kautz
Controllable Invariance through Adversarial Feature Learning
Qizhe Xie - Zihang Dai - Yulun Du - Eduard Hovy - Graham Neubig
Adversarial Ranking for Language Generation
Kevin Lin - Dianqi Li - Xiaodong He - Ming-ting Sun - Zhengyou Zhang
Linear Time Computation of Moments in Sum-Product Networks
Han Zhao - Geoffrey Gordon
Attention is All you Need
Ashish Vaswani - Noam Shazeer - Niki Parmar - Jakob Uszkoreit - Llion Jones - Aidan N Gomez - Łukasz Kaiser - Illia Polosukhin
Masked Autoregressive Flow for Density Estimation
George Papamakarios - Iain Murray - Theo Pavlakou
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Anirudh Goyal ALIAS PARTH GOYAL - Nan Ke - Surya Ganguli - Yoshua Bengio
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen - Cong Xu - Feng Yan - Chunpeng Wu - Yandan Wang - Yiran Chen - Hai Li
A simple neural network module for relational reasoning
Adam Santoro - David Raposo - David Barrett - Mateusz Malinowski - Razvan Pascanu - Peter Battaglia - Tim Lillicrap
Dual Path Networks
Yunpeng Chen - Jianan Li - Huaxin Xiao - Xiaojie Jin - Shuicheng Yan - Jiashi Feng
Deep Sets
Manzil Zaheer - Satwik Kottur - Siamak Ravanbakhsh - Barnabas Poczos - Ruslan Salakhutdinov - Alexander Smola
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan - Alexander Pritzel - Charles Blundell
Self-Normalizing Neural Networks
Günter Klambauer - Thomas Unterthiner - Andreas Mayr - Sepp Hochreiter
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer - Itay Hubara - Daniel Soudry
Distral: Robust multitask reinforcement learning
Yee Teh - Victor Bapst - Wojciech M. Czarnecki - John Quan - James Kirkpatrick - Raia Hadsell - Nicolas Heess - Razvan Pascanu
Learning Combinatorial Optimization Algorithms over Graphs
Elias Khalil - Hanjun Dai - Yuyu Zhang - Bistra Dilkina - Le Song
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Chengxu Zhuang - Jonas Kubilius - Mitra JZ Hartmann - Daniel Yamins
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser - Jinyao Yan - Evan Archer - Lars Buesing - Srinivas C Turaga - Jakob H Macke
Deep Hyperalignment
Muhammad Yousefnezhad - Daoqiang Zhang
Online Dynamic Programming
Holakou Rahmanian - Manfred Warmuth
Unsupervised Learning of Disentangled Representations from Video
Emily Denton - vighnesh Birodkar
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
Slobodan Mitrovic - Ilija Bogunovic - Ashkan Norouzi-Fard - Jakub M Tarnawski - Volkan Cevher
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C Wilson - Rebecca Roelofs - Mitchell Stern - Nati Srebro - Benjamin Recht
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa - Rémi Leblond - Simon Lacoste-Julien
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
Omar El Housni - Vineet Goyal
Approximate Supermodularity Bounds for Experimental Design
Luiz Chamon - Alejandro Ribeiro
Asynchronous Coordinate Descent under More Realistic Assumptions
Tao Sun - Robert Hannah - Wotao Yin
Clustering with Noisy Queries
Arya Mazumdar - Barna Saha
Approximation Algorithms for-Low Rank Approximation
Karl Bringmann - Pavel Kolev - David Woodruff
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Yuanzhi Li - Yang Yuan
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian - Ce Zhang - Huan Zhang - Cho-Jui Hsieh - Wei Zhang - Ji Liu
Straggler Mitigation in Distributed Optimization Through Data Encoding
Can Karakus - Yifan Sun - Suhas Diggavi - Wotao Yin
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex
Chaobing Song - Shaobo Cui - Yong Jiang - Shu-Tao Xia
Safe Adaptive Importance Sampling
Sebastian U Stich - Anant Raj - Martin Jaggi
Sharpness, Restart and Acceleration
Vincent Roulet - Alexandre d'Aspremont
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Alberto Bietti - Julien Mairal
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro - Simon Kamronn - Ulrich Paquet - Ole Winther
Concrete Dropout
Yarin Gal - Jiri Hron - Alex Kendall
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker - Andriy Mnih - Chris J Maddison - John Lawson - Jascha Sohl-Dickstein
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran - Rajesh Ranganath - David Blei
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder - Yuhuai Wu - David Duvenaud
Perturbative Black Box Variational Inference
Robert Bamler - Cheng Zhang - Manfred Opper - Stephan Mandt
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier - Michael Jordan - Jon McAuliffe
Permutation-based Causal Inference Algorithms with Interventions
Yuhao Wang - Liam Solus - Karren Yang - Caroline Uhler
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami - Saber Salehkaleybar - Negar Kiyavash - Kun Zhang
Counterfactual Fairness
Matt Kusner - Joshua Loftus - Chris Russell - Ricardo Silva
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos - Uri Shalit - Joris M Mooij - David Sontag - Richard Zemel - Max Welling
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Mikhail Yurochkin - Aritra Guha - XuanLong Nguyen
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan Huggins - Ryan Adams - Tamara Broderick
Adaptive Bayesian Sampling with Monte Carlo EM
Anirban Roychowdhury - Srinivasan Parthasarathy
Reliable Decision Support using Counterfactual Models
Peter Schulam - Suchi Saria
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Hugh Salimbeni - Marc Deisenroth
Convolutional Gaussian Processes
Mark van der Wilk - Carl Edward Rasmussen - James Hensman
Multiresolution Kernel Approximation for Gaussian Process Regression
Yi Ding - Risi Kondor - Jonathan Eskreis-Winkler
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann - Tor Lattimore - Emma Brunskill
Repeated Inverse Reinforcement Learning
Kareem Amin - Nan Jiang - Satinder Singh
Inverse Reward Design
Dylan Hadfield-Menell - Smitha Milli - Pieter Abbeel - Stuart J Russell - Anca Dragan
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot - Vinicius Zambaldi - Audrunas Gruslys - Angeliki Lazaridou - karl Tuyls - Julien Perolat - David Silver - Thore Graepel
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi - Rachid Guerraoui - Hadrien Hendrikx - Alexandre Maurer
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe - YI WU - Aviv Tamar - Jean Harb - OpenAI Pieter Abbeel - Igor Mordatch
Spectrally-normalized margin bounds for neural networks
Peter Bartlett - Dylan J Foster - Matus Telgarsky
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin - Francis Bach - Simon Lacoste-Julien
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Noga Alon - Moshe Babaioff - Yannai A. Gonczarowski - Yishay Mansour - Shay Moran - Amir Yehu'day'off
Variance-based Regularization with Convex Objectives
Hongseok Namkoong - John C Duchi
Learning Mixture of Gaussians with Streaming Data
Aditi Raghunathan - Prateek Jain - Ravishankar Krishnawamy
On the Consistency of Quick Shift
Heinrich Jiang
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Yuting Wei - Fanny Yang - Martin Wainwright
The Scaling Limit of High-Dimensional Online Independent Component Analysis
Chuang Wang - Yue Lu
Online control of the false discovery rate with decaying memory
Aaditya Ramdas - Fanny Yang - Martin Wainwright - Michael Jordan
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Roel Dobbe - David Fridovich-Keil - Claire Tomlin
Revenue Optimization with Approximate Bid Predictions
Andres Munoz - Sergei Vassilvitskii
A Decomposition of Forecast Error in Prediction Markets
Miro Dudik - Sebastien Lahaie - Ryan M Rogers - Jennifer Wortman Vaughan
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