Highlight



Session


Active Learning


   Paper Name    Author
On The Relationship Between Data Efficiency And Error For Uncertainty Sampling
Steve Mussmann;Percy Liang;
Design Of Experiments For Model Discrimination Hybridising Analytical And Data-Driven Approaches
Simon Olofsson;Marc P Deisenroth;Ruth Misener;
Selecting Representative Examples For Program Synthesis
Yewen Pu;Zachery Miranda;Armando Solar-Lezama;Leslie Kaelbling;


Approximate Inference


   Paper Name    Author
Quasi-Monte Carlo Variational Inference
Alexander Buchholz;Florian Wenzel;Stephan Mandt;
Efficient Gradient-Free Variational Inference Using Policy Search
Oleg Arenz;Gerhard Neumann;Mingjun Zhong;
A Spectral Approach To Gradient Estimation For Implicit Distributions
Jiaxin Shi;Shengyang Sun;Jun Zhu;
Semi-Implicit Variational Inference
Mingzhang Yin;Mingyuan Zhou;


Causal Inference


   Paper Name    Author
Budgeted Experiment Design For Causal Structure Learning
AmirEmad Ghassami;Saber Salehkaleybar;Negar Kiyavash;Elias Bareinboim;
Causal Bandits With Propagating Inference
Akihiro Yabe;Daisuke Hatano;Hanna Sumita;Shinji Ito;Naonori Kakimura;Takuro Fukunaga;Ken-ichi Kawarabayashi;
The Hierarchical Adaptive Forgetting Variational Filter
Vincent Moens;
Characterizing And Learning Equivalence Classes Of Causal DAGs Under Interventions
Karren Yang;Abigail Katoff;Caroline Uhler;


Clustering




Computer Vision


   Paper Name    Author
One-Shot Segmentation In Clutter
Claudio Michaelis;Matthias Bethge;Alexander Ecker;
Neural Inverse Rendering For General Reflectance Photometric Stereo
Tatsunori Taniai;Takanori Maehara;
Neural Program Synthesis From Diverse Demonstration Videos
Shao-Hua Sun;Hyeonwoo Noh;Sriram Somasundaram;Joseph Lim;
Generalized Earley Parser: Bridging Symbolic Grammars And Sequence Data For Future Prediction
Siyuan Qi;Baoxiong Jia;Song-Chun Zhu;
Deep Predictive Coding Network For Object Recognition
Haiguang Wen;Kuan Han;Junxing Shi;Yizhen Zhang;Eugenio Culurciello;Zhongming Liu;
Active Testing: An Efficient And Robust Framework For Estimating Accuracy
Phuc Nguyen;Deva Ramanan;Charless Fowlkes;
Video Prediction With Appearance And Motion Conditions
Yunseok Jang;Gunhee Kim;Yale Song;
Solving Partial Assignment Problems Using Random Clique Complexes
Charu Sharma;Deepak Nathani;Manu Kaul;
Gradually Updated Neural Networks For Large-Scale Image Recognition
Siyuan Qiao;Zhishuai Zhang;Wei Shen;Bo Wang;Alan Yuille;


Deep Learning (Adversarial)


   Paper Name    Author
Is Generator Conditioning Causally Related To GAN Performance?
Augustus Odena;Jacob Buckman;Catherine Olsson;Tom B Brown;Christopher Olah;Colin Raffel;Ian Goodfellow;
GAIN: Missing Data Imputation Using Generative Adversarial Nets
Jinsung Yoon;James Jordon;Mihaela van der Schaar;
The Mechanics Of N-Player Differentiable Games
David Balduzzi;Sebastien Racaniere;James Martens;Jakob Foerster;Karl Tuyls;Thore Graepel;
Tempered Adversarial Networks
Mehdi S. M. Sajjadi;Giambattista Parascandolo;Arash Mehrjou;Bernhard Sch?lkopf;
Augmented CycleGAN: Learning Many-to-Many Mappings From Unpaired Data
Amjad Almahairi;Sai Rajeswar;Alessandro Sordoni;Philip Bachman;Aaron Courville;
LaVAN: Localized And Visible Adversarial Noise
Danny Karmon;Daniel Zoran;Yoav Goldberg;
Obfuscated Gradients Give A False Sense Of Security: Circumventing Defenses To Adversarial Examples
Anish Athalye;Nicholas Carlini;David Wagner;
Adversarial Attack On Graph Structured Data
Hanjun Dai;Hui Li;Tian Tian;huangxin Huang;Lin Wang;Jun Zhu;Le Song;
Composite Functional Gradient Learning Of Generative Adversarial Models
Rie Johnson;Tong Zhang;
Adversarially Regularized Autoencoders
Jake Zhao;Yoon Kim;Kelly Zhang;Alexander Rush;Yann LeCun;
Improved Training Of Generative Adversarial Networks Using Representative Features
Duhyeon Bang;Hyunjung Shim;
A Two-Step Computation Of The Exact GAN Wasserstein Distance
Huidong Liu;Xianfeng GU;Samaras Dimitris;
First Order Generative Adversarial Networks
Calvin Seward;Thomas Unterthiner;Urs M Bergmann;Nikolay Jetchev;Sepp Hochreiter;
Towards Fast Computation Of Certified Robustness For ReLU Networks
Lily Weng;Huan Zhang;Hongge Chen;Zhao Song;Cho-Jui Hsieh;Luca Daniel;Duane Boning;Inderjit Dhillon;
JointGAN: Multi-Domain Joint Distribution Learning With Generative Adversarial Nets
Yunchen Pu;Shuyang Dai;Zhe Gan;Weiyao Wang;Guoyin Wang;Yizhe Zhang;Ricardo Henao;Lawrence Carin;
Mixed Batches And Symmetric Discriminators For GAN Training
Thomas LUCAS;Corentin Tallec;Yann Ollivier;Jakob Verbeek;
Black-box Adversarial Attacks With Limited Queries And Information
Andrew Ilyas;Logan Engstrom;Anish Athalye;Jessy Lin;
Mutual Information Neural Estimation
Mohamed Ishmael Belghazi;Aristide Baratin;Sai Rajeswar;Sherjil Ozair;Yoshua Bengio;R Devon Hjelm;Aaron Courville;
K-Beam Minimax: Efficient Optimization For Deep Adversarial Learning
Jihun Hamm;Yung-Kyun Noh;


Deep Learning (Bayesian)


   Paper Name    Author
Variational Inference And Model Selection With Generalized Evidence Bounds
Liqun Chen;Chenyang Tao;RUIYI ZHANG;Ricardo Henao;Lawrence Carin;
Decomposition Of Uncertainty In Bayesian Deep Learning For Efficient And Risk-sensitive Learning
Stefan Depeweg;Jose Hernandez-Lobato;Finale Doshi-Velez;Steffen Udluft;
Variational Bayesian Dropout: Pitfalls And Fixes
Jiri Hron;Alex Matthews;Zoubin Ghahramani;
Fixing A Broken ELBO
Alex Alemi;Ben Poole;Ian Fischer;Josh V Dillon;Rif Saurous;Kevin Murphy;
Continuous-Time Flows For Efficient Inference And Density Estimation
Changyou Chen;Chunyuan Li;Liquan Chen;Wenlin Wang;Yunchen Pu;Lawrence Carin;
Accurate Uncertainties For Deep Learning Using Calibrated Regression
Volodymyr Kuleshov;Nathan Fenner;Stefano Ermon;
Tighter Variational Bounds Are Not Necessarily Better
Tom Rainforth;Adam Kosiorek;Tuan Anh Le;Chris Maddison;Max Igl;Frank Wood;Yee Whye Teh;
Scalable Approximate Bayesian Inference For Particle Tracking Data
Ruoxi Sun;Department of Statistics Liam Paninski;
Fast And Scalable Bayesian Deep Learning By Weight-Perturbation In Adam
Emti Khan;Didrik Nielsen;Voot Tangkaratt;Wu Lin;Yarin Gal;Akash Srivastava;


Deep Learning (Neural Network Architectures)


   Paper Name    Author
Efficient Neural Audio Synthesis
Nal Kalchbrenner;Erich Elsen;Karen Simonyan;Seb Noury;Norman Casagrande;Edward Lockhart;Florian Stimberg;A?ron van den Oord;Sander Dieleman;koray kavukcuoglu;
Fast Decoding In Sequence Models Using Discrete Latent Variables
Lukasz Kaiser;Samy Bengio;Aurko Roy;Ashish Vaswani;Niki Parmar;Jakob Uszkoreit;Noam Shazeer;
Compressing Neural Networks Using The Variational Information Bottelneck
Bin Dai;Chen Zhu;Baining Guo;David Wipf;
TACO: Learning Task Decomposition Via Temporal Alignment For Control
Kyriacos Shiarlis;Markus Wulfmeier;Sasha Salter;Shimon Whiteson;Ingmar Posner;
RadialGAN: Leveraging Multiple Datasets To Improve Target-specific Predictive Models Using Generative Adversarial Networks
Jinsung Yoon;James Jordon;Mihaela van der Schaar;
Differentiable Plasticity: Training Plastic Neural Networks With Backpropagation
Thomas Miconi;Ken Stanley;Jeff Clune;
Path-Level Network Transformation For Efficient Architecture Search
Han Cai;Jiacheng Yang;Weinan Zhang;Song Han;Yong Yu;
A Semantic Loss Function For Deep Learning With Symbolic Knowledge
Jingyi Xu;Zilu Zhang;Tal Friedman;Yitao Liang;Guy Van den Broeck;
Using Inherent Structures To Design Lean 2-layer RBMs
Abhishek Bansal;Abhinav Anand;Chiru Bhattacharyya;
Neural Dynamic Programming For Musical Self Similarity
Christian Walder;Dongwoo Kim;
Rapid Adaptation With Conditionally Shifted Neurons
Tsendsuren Munkhdalai;Xingdi Yuan;Soroush Mehri;Adam Trischler;
Learning Long Term Dependencies Via Fourier Recurrent Units
Jiong Zhang;Yibo Lin;Zhao Song;Inderjit Dhillon;
Not To Cry Wolf: Distantly Supervised Multitask Learning In Critical Care
Patrick Schwab;Emanuela Keller;Carl Muroi;David J. Mack;Christian Str?ssle;Walter Karlen;
Adafactor: Adaptive Learning Rates With Sublinear Memory Cost
Noam Shazeer;Mitchell Stern;
Understanding And Simplifying One-Shot Architecture Search
gbender Bender;Pieter-Jan Kindermans;Barret Zoph;Vijay Vasudevan;Quoc Le;
Learning To Search With MCTSnets
Arthur Guez;Theo Weber;Ioannis Antonoglou;Karen Simonyan;Oriol Vinyals;Daan Wierstra;Remi Munos;David Silver;
Orthogonal Recurrent Neural Networks With Scaled Cayley Transform
Kyle Helfrich;Devin Willmott;Qiang Ye;
Progress & Compress: A Scalable Framework For Continual Learning
Jonathan Schwarz;Wojciech Czarnecki;Jelena Luketina;Agnieszka Grabska-Barwinska;Yee Teh;Razvan Pascanu;Raia Hadsell;
Deep Models Of Interactions Across Sets
Jason Hartford;Devon Graham;Kevin Leyton-Brown;Siamak Ravanbakhsh;
Hierarchical Long-term Video Prediction Without Supervision
Nevan Wichers;Ruben Villegas;Dumitru Erhan;Honglak Lee;
Conditional Neural Processes
Marta Garnelo;Dan Rosenbaum;Chris Maddison;Tiago Ramalho;David Saxton;Murray Shanahan;Yee Teh;Danilo J. Rezende;S. M. Ali Eslami;
Kernelized Synaptic Weight Matrices
Lorenz Mȹller;Julien Martel;Giacomo Indiveri;
A Hierarchical Latent Vector Model For Learning Long-Term Structure In Music
Adam Roberts;JesseEngel Engel;Colin Raffel;Curtis "Fjord" Hawthorne;Douglas Eck;
Graph Networks As Learnable Physics Engines For Inference And Control
Alvaro Sanchez;Nicolas Heess;Jost Springenberg;Josh Merel;Martin Riedmiller;Raia Hadsell;Peter Battaglia;
Learn From Your Neighbor: Learning Multi-modal Mappings From Sparse Annotations
Ashwin Kalyan;Stefan Lee;Anitha Kannan;Dhruv Batra;
Extracting Automata From Recurrent Neural Networks Using Queries And Counterexamples
Gail Weiss;Yoav Goldberg;Eran Yahav;
Non-linear Motor Control By Local Learning In Spiking Neural Networks
Aditya Gilra;Wulfram Gerstner;
Fast Parametric Learning With Activation Memorization
Jack Rae;Chris Dyer;Peter Dayan;Tim Lillicrap;
Gradient-Based Meta-Learning With Learned Layerwise Metric And Subspace
Yoonho Lee;Seungjin Choi;
Training Neural Machines With Trace-Based Supervision
Matthew Mirman;Dimitar Dimitrov;Pavle Djordjevic;Timon Gehr;Martin Vechev;
Exploiting The Potential Of Standard Convolutional Autoencoders For Image Restoration By Evolutionary Search
Masanori SUGANUMA;Mete Ozay;Takayuki Okatani;
High Performance Zero-Memory Overhead Direct Convolutions
Jiyuan Zhang;Franz Franchetti;Tze Meng Low;
Focused Hierarchical RNNs For Conditional Sequence Processing
Rosemary Nan Ke;Konrad Zolna;Alessandro Sordoni;MILA Zhouhan Lin;Adam Trischler;Yoshua Bengio;Joelle Pineau;Laurent Charlin;Christopher Pal;
Kronecker Recurrent Units
Cijo Jose;Moustapha Cisse;Francois Fleuret;
Overcoming Catastrophic Forgetting With Hard Attention To The Task
Joan SerrȤ;Didac Suris;Marius Miron;Alexandros Karatzoglou;
Image Transformer
Niki Parmar;Ashish Vaswani;Jakob Uszkoreit;Lukasz Kaiser;Noam Shazeer;Alexander Ku;Dustin Tran;
Beyond Finite Layer Neural Networks: Bridging Deep Architectures And Numerical Differential Equations
Yiping Lu;Aoxiao Zhong;Quanzheng Li;Bin Dong;
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster;Gregory Farquhar;Maruan Al-Shedivat;Tim Rockt?schel;Eric Xing;Shimon Whiteson;
Model-Level Dual Learning
Yingce Xia;Xu Tan;Fei Tian;Tao Qin;Nenghai Yu;Tie-Yan Liu;
PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen;Nikhil Mishra;Mostafa Rohaninejad;Pieter Abbeel;
Learning Longer-term Dependencies In RNNs With Auxiliary Losses
Trieu H Trinh;Andrew Dai;Thang Luong;Quoc Le;
Deep Asymmetric Multi-task Feature Learning
Hae Beom Lee;Eunho Yang;Sung Ju Hwang;
Dynamic Evaluation Of Neural Sequence Models
Ben Krause;Emmanuel Kahembwe;Iain Murray;Steve Renals;
PredRNN++: Towards A Resolution Of The Deep-in-Time Dilemma In Spatiotemporal Predictive Learning
Yunbo Wang;Zhifeng Gao;Mingsheng Long;Jianmin Wang;Philip Yu;


Deep Learning (Theory)


   Paper Name    Author
Stronger Generalization Bounds For Deep Nets Via A Compression Approach
Sanjeev Arora;Rong Ge;Behnam Neyshabur;Yi Zhang;
A Boo(n) For Evaluating Architecture Performance
Ondrej Bajgar;Rudolf Kadlec;Jan Kleindienst;
Gradient Descent Learns One-hidden-layer CNN: Don't Be Afraid Of Spurious Local Minima
Simon Du;Jason Lee;Yuandong Tian;Aarti Singh;BarnabȢs PȮczos;
High-Quality Prediction Intervals For Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce;Alexandra Brintrup;Mohamed Zaki;Andy Neely;
On The Optimization Of Deep Networks: Implicit Acceleration By Overparameterization
Sanjeev Arora;Nadav Cohen;Elad Hazan;
Spurious Local Minima Are Common In Two-Layer ReLU Neural Networks
Itay Safran;Ohad Shamir;
The Multilinear Structure Of ReLU Networks
Thomas Laurent;James von Brecht;
Deep Linear Networks With Arbitrary Loss: All Local Minima Are Global
Thomas Laurent;James von Brecht;
On The Power Of Over-parametrization In Neural Networks With Quadratic Activation
Simon Du;Jason Lee;
Optimization Landscape And Expressivity Of Deep CNNs
Quynh Nguyen;Matthias Hein;
The Dynamics Of Learning: A Random Matrix Approach
Zhenyu Liao;Romain Couillet;
Tropical Geometry Of Deep Neural Networks
Liwen Zhang;Gregory Naisat;Lek-Heng Lim;
Dynamical Isometry And A Mean Field Theory Of RNNs: Gating Enables Signal Propagation In Recurrent Neural Networks
Minmin Chen;Jeffrey Pennington;Samuel Schoenholz;
A Spline Theory Of Deep Learning
Randall Balestriero;Richard Baraniuk;
Understanding Generalization And Optimization Performance Of Deep CNNs
Pan Zhou;Jiashi Feng;
Gradient Descent With Identity Initialization Efficiently Learns Positive Definite Linear Transformations By Deep Residual Networks
Peter Bartlett;Dave Helmbold;Phil Long;
Neural Networks Should Be Wide Enough To Learn Disconnected Decision Regions
Quynh Nguyen;Mahesh Mukkamala;Matthias Hein;
Entropy-SGD Optimizes The Prior Of A PAC-Bayes Bound: Generalization Properties Of Entropy-SGD And Data-dependent Priors
Gintare Karolina Dziugaite;Dan Roy;
Learning One Convolutional Layer With Overlapping Patches
Surbhi Goel;Adam Klivans;Raghu Meka;
Invariance Of Weight Distributions In Rectified MLPs
Russell Tsuchida;Fred Roosta;Marcus Gallagher;
Learning Dynamics Of Linear Denoising Autoencoders
Arnu Pretorius;Steve Kroon;Herman Kamper;
Efficient End-to-end Learning For Quantizable Representations
Yeonwoo Jeong;Hyun Oh Song;
Understanding The Loss Surface Of Neural Networks For Binary Classification
SHIYU LIANG;Ruoyu Sun;Yixuan Li;R Srikant;
On The Limitations Of First-Order Approximation In GAN Dynamics
Jerry Li;Aleksander Madry;John Peebles;Ludwig Schmidt;
Reviving And Improving Recurrent Back-Propagation
Renjie Liao;Yuwen Xiong;Ethan Fetaya;Lisa Zhang;KiJung Yoon;xaq S Pitkow;Raquel Urtasun;Richard Zemel;


Dimensionality Reduction


   Paper Name    Author
Bayesian Model Selection For Change Point Detection And Clustering
othmane mazhar;Cristian R. Rojas;Inst. of Technology Carlo Fischione;Mohammad Reza Hesamzadeh;
Streaming Principal Component Analysis In Noisy Setting
Teodor Vanislavov Marinov;Poorya Mianjy;Raman Arora;
Provable Variable Selection For Streaming Features
Jing Wang;Jie Shen;Ping Li;
Out-of-sample Extension Of Graph Adjacency Spectral Embedding
Keith Levin;Fred Roosta;Michael Mahoney;Carey Priebe;
Stochastic PCA With $\ell_2$ And $\ell_1$ Regularization
Poorya Mianjy;Raman Arora;
Learning Low-Dimensional Temporal Representations
Bing Su;Ying Wu;
An Iterative, Sketching-based Framework For Ridge Regression
Agniva Chowdhury;Jiasen Yang;Petros Drineas;
Subspace Embedding And Linear Regression With Orlicz Norm
Alexandr Andoni;Chengyu Lin;Ying Sheng;Peilin Zhong;Ruiqi Zhong;
Leveraging Well-Conditioned Bases: Streaming And Distributed Summaries In Minkowski $p$-Norms
Charlie Dickens;Graham Cormode;David Woodruff;


Feature Selection


   Paper Name    Author
Nonoverlap-Promoting Variable Selection
Pengtao Xie;Hongbao Zhang;Yichen Zhu;Eric Xing;
Learning To Explain: An Information-Theoretic Perspective On Model Interpretation
Jianbo Chen;Le Song;Martin Wainwright;Michael Jordan;
Black Box FDR
Wesley Tansey;Yixin Wang;David Blei;Raul Rabadan;
Variable Selection Via Penalized Neural Network: A Drop-Out-One Loss Approach
Mao Ye;Yan Sun;
MSplit LBI: Realizing Feature Selection And Dense Estimation Simultaneously In Few-shot And Zero-shot Learning
Bo Zhao;Xinwei Sun;Yanwei Fu;Yuan Yao;Yizhou Wang;


Gaussian Processes


   Paper Name    Author
Markov Modulated Gaussian Cox Processes For Semi-Stationary Intensity Modeling Of Events Data
Minyoung Kim;
Large-Scale Cox Process Inference Using Variational Fourier Features
Ti John;James Hensman;
Probabilistic Recurrent State-Space Models
Andreas Doerr;Christian Daniel;Martin Schiegg;Duy Nguyen-Tuong;Stefan Schaal;Marc Toussaint;Sebastian Trimpe;
Constraining The Dynamics Of Deep Probabilistic Models
Marco Lorenzi;Maurizio Filippone;
Bayesian Quadrature For Multiple Related Integrals
Xiaoyue Xi;Francois-Xavier Briol;Mark Girolami;
Constant-Time Predictive Distributions For Gaussian Processes
Geoff Pleiss;Jacob Gardner;Kilian Weinberger;Andrew Wilson;
Scalable Gaussian Processes With Grid-Structured Eigenfunctions (GP-GRIEF)
Trefor Evans;Prasanth B Nair;
State Space Gaussian Processes With Non-Gaussian Likelihood
Hannes Nickisch;Arno Solin;Alexander Grigorevskiy;
Generalized Robust Bayesian Committee Machine For Large-scale Gaussian Process Regression
Haitao Liu;Jianfei Cai;Yi Wang;Yew Soon ONG;
Structured Variational Learning Of Bayesian Neural Networks With Horseshoe Priors
Soumya Ghosh;Jiayu Yao;Finale Doshi-Velez;
Learning Unknown ODE Models With Gaussian Processes
Markus Heinonen;Cagatay Yildiz;Henrik Mannerstr?m;Jukka Intosalmi;Harri L?hdesm?ki;
Differentiable Compositional Kernel Learning For Gaussian Processes
Shengyang Sun;Guodong Zhang;Chaoqi Wang;Wenyuan Zeng;Jiaman Li;Roger Grosse;


Generative Models


   Paper Name    Author
Junction Tree Variational Autoencoder For Molecular Graph Generation
Wengong Jin;Regina Barzilay;Tommi Jaakkola;
DVAE++: Discrete Variational Autoencoders With Overlapping Transformations
Arash Vahdat;William Macready;Zhengbing Bian;Amir Khoshaman;Evgeny Andriyash;
Optimizing The Latent Space Of Generative Networks
Piotr Bojanowski;Armand Joulin;David Lopez-Paz;Arthur Szlam;
Which Training Methods For GANs Do Actually Converge?
Lars Mescheder;Andreas Geiger;Sebastian Nowozin;
Adversarial Learning With Local Coordinate Coding
Jiezhang Cao;Yong Guo;Qingyao Wu;Chunhua Shen;Junzhou Huang;Mingkui Tan;
Geometry Score: A Method For Comparing Generative Adversarial Networks
Valentin Khrulkov;Ivan Oseledets;
Disentangled Sequential Autoencoder
Yingzhen Li;Stephan Mandt;
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
A?ron van den Oord;Yazhe Li;Igor Babuschkin;Karen Simonyan;Oriol Vinyals;koray kavukcuoglu;George van den Driessche;Edward Lockhart;Luis C Cobo;Florian Stimberg;Norman Casagrande;Dominik Grewe;Seb Noury;Sander Dieleman;Erich Elsen;Nal Kalchbrenner;Heiga Zen;Alex Graves;Helen King;Tom Walters;Dan Belov;Demis Hassabis;
Learning Implicit Generative Models With The Method Of Learned Moments
Suman Ravuri;Shakir Mohamed;Mihaela Rosca;Oriol Vinyals;
Stochastic Video Generation With A Learned Prior
Emily Denton;Rob Fergus;
Learning Representations And Generative Models For 3D Point Clouds
Panos Achlioptas;Olga Diamanti;Ioannis Mitliagkas;Leonidas Guibas;
Semi-Amortized Variational Autoencoders
Yoon Kim;Sam Wiseman;Andrew Miller;David Sontag;Alexander Rush;
Autoregressive Quantile Networks For Generative Modeling
Georg Ostrovski;Will Dabney;Remi Munos;
Chi-square Generative Adversarial Network
Chenyang Tao;Liqun Chen;Ricardo Henao;Jianfeng Feng;Lawrence Carin;
Iterative Amortized Inference
Joe Marino;Yisong Yue;Stephan Mandt;
A Classification-Based Study Of Covariate Shift In GAN Distributions
Shibani Santurkar;Ludwig Schmidt;Aleksander Madry;


Graphical Models


   Paper Name    Author
Sound Abstraction And Decomposition Of Probabilistic Programs
Steven Holtzen;Guy Van den Broeck;Todd Millstein;
Robust And Scalable Models Of Microbiome Dynamics
Travis Gibson;Georg Gerber;
Temporal Poisson Square Root Graphical Models
Sinong Geng;Charles Kuang;Peggy Peissig;University of Wisconsin David Page;
Stein Variational Message Passing For Continuous Graphical Models
Dilin Wang;Zhe Zeng;Qiang Liu;
Learning In Integer Latent Variable Models With Nested Automatic Differentiation
Daniel Sheldon;Kevin Winner;Debora Sujono;
The Edge Density Barrier: Computational-Statistical Tradeoffs In Combinatorial Inference
Hao Lu;Yuan Cao;Junwei Lu;Han Liu;Zhaoran Wang;
Parallel Bayesian Network Structure Learning
Tian Gao;Dennis Wei;


Kernel Methods




Large Scale Learning and Big Data


   Paper Name    Author
Fast Approximate Spectral Clustering For Dynamic Networks
Lionel Martin;Andreas Loukas;Pierre Vandergheynst;
Matrix Norms In Data Streams: Faster, Multi-Pass And Row-Order
Vladimir Braverman;Stephen Chestnut;Robert Krauthgamer;Yi Li;David Woodruff;Lin Yang;
Approximate Leave-One-Out For Fast Parameter Tuning In High Dimensions
Shuaiwen Wang;Wenda Zhou;Haihao Lu;Arian Maleki;Vahab Mirrokni;
Improved Large-scale Graph Learning Through Ridge Spectral Sparsification
Daniele Calandriello;Alessandro Lazaric;Ioannis Koutis;Michal Valko;
Semi-Supervised Learning On Data Streams Via Temporal Label Propagation
Tal Wagner;Sudipto Guha;Shiva Kasiviswanathan;Nina Mishra;
Loss Decomposition For Fast Learning In Large Output Spaces
Ian Yen;Satyen Kale;Felix Xinnan Yu;Daniel Holtmann-Rice;Sanjiv Kumar;Pradeep Ravikumar;
Parallel And Streaming Algorithms For K-Core Decomposition
Hossein Esfandiari;Silvio Lattanzi;Vahab Mirrokni;
Near Optimal Frequent Directions For Sketching Dense And Sparse Matrices
Zengfeng Huang;
Ultra Large-Scale Feature Selection Using Count-Sketches
Amirali Aghazadeh;Ryan Spring;Daniel LeJeune;Gautam Dasarathy;Anshumali Shrivastava;Richard Baraniuk;


Matrix Factorization


   Paper Name    Author
Probabilistic Boolean Tensor Decomposition
Tammo Rukat;Christopher Holmes;Christopher Yau;
A Primal-Dual Analysis Of Global Optimality In Nonconvex Low-Rank Matrix Recovery
Xiao Zhang;Lingxiao Wang;Yaodong Yu;Quanquan Gu;
Closed-form Marginal Likelihood In Gamma-Poisson Matrix Factorization
Louis Filstroff;Alberto Lumbreras;Cedric Fevotte;
Implicit Regularization In Nonconvex Statistical Estimation: Gradient Descent Converges Linearly For Phase Retrieval And Matrix Completion
Cong Ma;Kaizheng Wang;Yuejie Chi;Yuxin Chen;
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Ariel Jaffe;Roi Weiss;Boaz Nadler;Shai Carmi;Yuval Kluger;


Monte Carlo Methods


   Paper Name    Author
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou;Pan Xu;Quanquan Gu;
Asynchronous Stochastic Quasi-Newton MCMC For Non-Convex Optimization
Umut Simsekli;Cagatay Yildiz;Thanh Huy Nguyen;Ali Taylan Cemgil;Ga?l RICHARD;
Discrete-Continuous Mixtures In Probabilistic Programming: Generalized Semantics And Inference Algorithms
Yi Wu;Siddharth Srivastava;Nicholas Hay;Simon Du;Stuart Russell;
On Nesting Monte Carlo Estimators
Tom Rainforth;Rob Cornish;Hongseok Yang;andrew warrington;Frank Wood;
On The Theory Of Variance Reduction For Stochastic Gradient Monte Carlo
Niladri S Chatterji;Nicolas Flammarion;Yian Ma;Peter Bartlett;Michael Jordan;
Error Estimation For Randomized Least-Squares Algorithms Via The Bootstrap
Miles Lopes;Shusen Wang;Michael Mahoney;
Stein Variational Gradient Descent Without Gradient
Jun Han;Qiang Liu;
A Robust Approach To Sequential Information Theoretic Planning
Sue Zheng;Jason Pacheco;John Fisher;
Minibatch Gibbs Sampling On Large Graphical Models
Chris De Sa;Zhiting Chen;Wong;


Multi-Agent Learning


   Paper Name    Author
QMIX: Monotonic Value Function Factorisation For Deep Multi-Agent Reinforcement Learning
Tabish Rashid;Mikayel Samvelyan;Christian Schroeder;Gregory Farquhar;Jakob Foerster;Shimon Whiteson;
Learning To Coordinate With Coordination Graphs In Repeated Single-Stage Multi-Agent Decision Problems
Eugenio Bargiacchi;Timothy Verstraeten;Diederik Roijers;Ann NowȦ;Hado van Hasselt;
Learning To Act In Decentralized Partially Observable MDPs
Jilles Dibangoye;Olivier Buffet;
Modeling Others Using Oneself In Multi-Agent Reinforcement Learning
Roberta Raileanu;Emily Denton;Arthur Szlam;Facebook Rob Fergus;
Learning Policy Representations In Multiagent Systems
Aditya Grover;Maruan Al-Shedivat;Jayesh Gupta;Yura Burda;Harrison Edwards;


Natural Language and Speech Processing


   Paper Name    Author
Analyzing Uncertainty In Neural Machine Translation
Myle Ott;Michael Auli;David Grangier;Marc'Aurelio Ranzato;
Hierarchical Text Generation And Planning For Strategic Dialogue
Denis Yarats;Mike Lewis;
Towards Binary-Valued Gates For Robust LSTM Training
Zhuohan Li;Di He;Fei Tian;Wei Chen;Tao Qin;Liwei Wang;Tie-Yan Liu;
Style Tokens: Unsupervised Style Modeling, Control And Transfer In End-to-End Speech Synthesis
Yuxuan Wang;Daisy Stanton;Yu Zhang;RJ-Skerry Ryan;Eric Battenberg;Joel Shor;Ying Xiao;Ye Jia;Fei Ren;Rif Saurous;
Adaptive Sampled Softmax With Kernel Based Sampling
Guy Blanc;Steffen Rendle;
Towards End-to-End Prosody Transfer For Expressive Speech Synthesis With Tacotron
RJ Skerry-Ryan;Eric Battenberg;Ying Xiao;Yuxuan Wang;Daisy Stanton;Joel Shor;Ron Weiss;Rob Clark;Rif Saurous;
Generalization Without Systematicity: On The Compositional Skills Of Sequence-to-Sequence Recurrent Networks
Brenden Lake;Marco Baroni;
Fitting New Speakers Based On A Short Untranscribed Sample
Eliya Nachmani;Adam Polyak;Yaniv Taigman;Lior Wolf;


Networks and Relational Learning


   Paper Name    Author
Learning Diffusion Using Hyperparameters
Dimitris Kalimeris;Yaron Singer;Karthik Subbian;Udi Weinsberg;
Representation Learning On Graphs With Jumping Knowledge Networks
Keyulu Xu;Chengtao Li;Yonglong Tian;Tomohiro Sonobe;Ken-ichi Kawarabayashi;Stefanie Jegelka;
Stochastic Training Of Graph Convolutional Networks With Variance Reduction
Jianfei Chen;Jun Zhu;Le Song;
Canonical Tensor Decomposition For Knowledge Base Completion
Timothee Lacroix;Nicolas Usunier;Guillaume R Obozinski;


Online Learning


   Paper Name    Author
Racing Thompson: An Efficient Algorithm For Thompson Sampling With Non-conjugate Priors
Yichi Zhou;Jun Zhu;Jingwei Zhuo;
Kernel Recursive ABC: Point Estimation With Intractable Likelihood
Takafumi Kajihara;Motonobu Kanagawa;Keisuke Yamazaki;Kenji Fukumizu;
Dynamic Regret Of Strongly Adaptive Methods
Lijun Zhang;Tianbao Yang;rong jin;Zhi-Hua Zhou;
Differentially Private Database Release Via Kernel Mean Embeddings
Matej Balog;Ilya Tolstikhin;Bernhard Sch?lkopf;
Feasible Arm Identification
Julian Katz-Samuels;Clay Scott;
Fast Stochastic AUC Maximization With $O(1/n)$-Convergence Rate
Mingrui Liu;Xiaoxuan Zhang;Zaiyi Chen;Xiaoyu Wang;Tianbao Yang;
Practical Contextual Bandits With Regression Oracles
Dylan Foster;Alekh Agarwal;Miroslav Dudik;Haipeng Luo;Robert Schapire;
Thompson Sampling For Combinatorial Semi-Bandits
Siwei Wang;Wei Chen;
Stochastic Proximal Algorithms For AUC Maximization
Michael Natole Jr;Yiming Ying;Siwei Lyu;
Adaptive Exploration-Exploitation Tradeoff For Opportunistic Bandits
Huasen Wu;Xueying Guo;Xin Liu;
Make The Minority Great Again: First-Order Regret Bound For Contextual Bandits
Zeyuan Allen-Zhu;Sebastien Bubeck;Yuanzhi Li;
Online Learning With Abstention
Corinna Cortes;Giulia DeSalvo;Claudio Gentile;Mehryar Mohri;Scott Yang;
Online Linear Quadratic Control
Alon Cohen;Avinatan Hasidim;Tomer Koren;Nevena Lazic;Yishay Mansour;Kunal Talwar;
Projection-Free Online Optimization With Stochastic Gradient: From Convexity To Submodularity
Lin Chen;Chris Harshaw;Hamed Hassani;Amin Karbasi;
Learning In Reproducing Kernel Kre??n Spaces
Dino Oglic;Thomas Gaertner;
Minimax Concave Penalized Multi-Armed Bandit Model With High-Dimensional Covariates
xue wang;Mike Wei;Tao Yao;
Semiparametric Contextual Bandits
Akshay Krishnamurthy;Zhiwei Wu;Vasilis Syrgkanis;
To Understand Deep Learning We Need To Understand Kernel Learning
Mikhail Belkin;Siyuan Ma;Soumik Mandal;
Firing Bandits: Optimizing Crowdfunding
Lalit Jain;Kevin Jamieson;
Bandits With Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke;Shipra Agrawal;Csaba Szepesvari;Steffen Grȹnew?lder;
Multi-Fidelity Black-Box Optimization With Hierarchical Partitions
Rajat Sen;kirthevasan kandasamy;Sanjay Shakkottai;


Optimization (Bayesian)


   Paper Name    Author
Stagewise Safe Bayesian Optimization With Gaussian Processes
Yanan Sui;Vincent Zhuang;Joel Burdick;Yisong Yue;
Fast Information-theoretic Bayesian Optimisation
Robin Ru;Michael A Osborne;Mark Mcleod;Diego Granziol;
Optimization, Fast And Slow: Optimally Switching Between Local And Bayesian Optimization
Mark McLeod;Stephen Roberts;Michael A Osborne;
Bayesian Optimization Of Combinatorial Structures
Ricardo Baptista;Matthias Poloczek;
BOHB: Robust And Efficient Hyperparameter Optimization At Scale
Stefan Falkner;Aaron Klein;Frank Hutter;
BOCK : Bayesian Optimization With Cylindrical Kernels
ChangYong Oh;Stratis Gavves;Max Welling;
Batch Bayesian Optimization Via Multi-objective Acquisition Ensemble For Automated Analog Circuit Design
Wenlong Lyu;Fan Yang;Changhao Yan;Dian Zhou;Xuan Zeng;
Tight Regret Bounds For Bayesian Optimization In One Dimension
Jonathan Scarlett;


Optimization (Combinatorial)


   Paper Name    Author
Compiling Combinatorial Prediction Games
Frederic Koriche;
Scalable Deletion-Robust Submodular Maximization: Data Summarization With Privacy And Fairness Constraints
Ehsan Kazemi;Morteza Zadimoghaddam;Amin Karbasi;
Competitive Caching With Machine Learned Advice
Thodoris Lykouris;Sergei Vassilvitskii;
Fast Maximization Of Non-Submodular, Monotonic Functions On The Integer Lattice
Alan Kuhnle;J. Smith;Victoria Crawford;My Thai;
Beyond 1/2-Approximation For Submodular Maximization On Massive Data Streams
Ashkan Norouzi-Fard;Jakub Tarnawski;Boba Mitrovic;Amir Zandieh;Aida Mousavifar;Ola Svensson;
Distributed Clustering Via LSH Based Data Partitioning
Aditya Bhaskara;Maheshakya Wijewardena;
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen;Moran Feldman;Amin Karbasi;
Decentralized Submodular Maximization: Bridging Discrete And Continuous Settings
Aryan Mokhtari;Hamed Hassani;Amin Karbasi;
Greed Is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai;Jeff Bilmes;
Learning To Branch
Nina Balcan;Travis Dick;Tuomas Sandholm;Ellen Vitercik;
Approximation Algorithms For Cascading Prediction Models
Matthew Streeter;
Constrained Interacting Submodular Groupings
Andrew Cotter;Mahdi Milani Fard;Seungil You;Maya Gupta;Jeff Bilmes;
Data Summarization At Scale: A Two-Stage Submodular Approach
Marko Mitrovic;Ehsan Kazemi;Morteza Zadimoghaddam;Amin Karbasi;
Approximation Guarantees For Adaptive Sampling
Eric Balkanski;Yaron Singer;


Optimization (Convex)


   Paper Name    Author
Frank-Wolfe With Subsampling Oracle
Thomas Kerdreux;Fabian Pedregosa;Alex d'Aspremont;
Level-Set Methods For Finite-Sum Constrained Convex Optimization
Qihang Lin;Runchao Ma;Tianbao Yang;
Gradient Coding From Cyclic MDS Codes And Expander Graphs
Netanel Raviv;Rashish Tandon;Alex Dimakis;Itzhak Tamo;
Accelerating Greedy Coordinate Descent Methods
Haihao Lu;Robert Freund;Vahab Mirrokni;
On Matching Pursuit And Coordinate Descent
Francesco Locatello;Anant Raj;Praneeth Karimireddy;Gunnar Raetsch;Bernhard Sch?lkopf;Sebastian Stich;Martin Jaggi;
Dissipativity Theory For Accelerating Stochastic Variance Reduction: A Unified Analysis Of SVRG And Katyusha Using Semidefinite Programs
Bin Hu;Stephen Wright;Laurent Lessard;
Computational Optimal Transport: Complexity By Accelerated Gradient Descent Is Better Than By Sinkhorn's Algorithm
Pavel Dvurechenskii;Alexander Gasnikov;Alexey Kroshnin;
Local Convergence Properties Of SAGA/Prox-SVRG And Acceleration
Clarice Poon;Jingwei Liang;Carola-Bibiane Sch?nlieb;
Characterizing Implicit Bias In Terms Of Optimization Geometry
Suriya Gunasekar;Jason Lee;Daniel Soudry;Nati Srebro;
A Delay-tolerant Proximal-Gradient Algorithm For Distributed Learning
Konstantin Mishchenko;Franck Iutzeler;JȦr?me Malick;Massih-Reza Amini;
Adaptive Three Operator Splitting
Fabian Pedregosa;Gauthier Gidel;
Continuous And Discrete-time Accelerated Stochastic Mirror Descent For Strongly Convex Functions
Pan Xu;Tianhao Wang;Quanquan Gu;
Fast Gradient-Based Methods With Exponential Rate: A Hybrid Control Framework
Arman Sharifi Kolarijani;Peyman Mohajerin Esfahani;Tamas Keviczky;
ADMM And Accelerated ADMM As Continuous Dynamical Systems
Guilherme Franca;Daniel Robinson;Rene Vidal;
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Zaiyi Chen;Yi Xu;Enhong Chen;Tianbao Yang;
Alternating Randomized Block Coordinate Descent
Jelena Diakonikolas;Orecchia Lorenzo;
An Efficient Semismooth Newton Based Algorithm For Convex Clustering
Yancheng Yuan;Defeng Sun;Kim-Chuan Toh;
A Distributed Second-Order Algorithm You Can Trust
Celestine Dȹnner;Aurelien Lucchi;Matilde Gargiani;An Bian;Thomas Hofmann;Martin Jaggi;
Randomized Block Cubic Newton Method
Nikita Doikov;Peter Richtarik;
Lyapunov Functions For First-Order Methods: Tight Automated Convergence Guarantees
Adrien Taylor;Bryan Van Scoy;Laurent Lessard;
A Conditional Gradient Framework For Composite Convex Minimization With Applications To Semidefinite Programming
Alp Yurtsever;Olivier Fercoq;Francesco Locatello;Volkan Cevher;
On Acceleration With Noise-Corrupted Gradients
Michael Cohen;Jelena Diakonikolas;Orecchia Lorenzo;
Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta;Tomer Koren;Yoram Singer;


Optimization (Non-convex)


   Paper Name    Author
Escaping Saddles With Stochastic Gradients
Hadi Daneshmand;Jonas Kohler;Aurelien Lucchi;Thomas Hofmann;
Estimation Of Markov Chain Via Rank-constrained Likelihood
XUDONG LI;Mengdi Wang;Anru Zhang;
PrDeep: Robust Phase Retrieval With A Flexible Deep Network
Christopher Metzler;Phil Schniter;Ashok Veeraraghavan;Richard Baraniuk;
Gradient Primal-Dual Algorithm Converges To Second-Order Stationary Solution For Nonconvex Distributed Optimization Over Networks
Mingyi Hong;Meisam Razaviyayn;Jason Lee;
An Alternative View: When Does SGD Escape Local Minima?
Bobby Kleinberg;Yuanzhi Li;Yang Yuan;
$D^2$: Decentralized Training Over Decentralized Data
Hanlin Tang;Xiangru Lian;Ming Yan;Ce Zhang;Ji Liu;
Non-convex Conditional Gradient Sliding
chao qu;Yan Li;Huan Xu;
Accelerating Natural Gradient With Higher-Order Invariance
Yang Song;Jiaming Song;Stefano Ermon;
Convergence Guarantees For A Class Of Non-convex And Non-smooth Optimization Problems
Koulik Khamaru;Martin Wainwright;
Dissecting Adam: The Sign, Magnitude And Variance Of Stochastic Gradients
Lukas Balles;Philipp Hennig;
Approximate Message Passing For Amplitude Based Optimization
Junjie Ma;Ji Xu;Arian Maleki;
A Progressive Batching L-BFGS Method For Machine Learning
Raghu Bollapragada;Jorge Nocedal;Dheevatsa Mudigere;Hao-Jun M Shi;Ping Tak Tang;


Other Applications


   Paper Name    Author
Learning Memory Access Patterns
Milad Hashemi;Kevin Swersky;Jamie Smith;Grant Ayers;Heiner Litz;Jichuan Chang;Christos Kozyrakis;Partha Ranganathan;
Variance Regularized Counterfactual Risk Minimization Via Variational Divergence Minimization
Hang Wu;May Wang;
TAPAS: Tricks To Accelerate (encrypted) Prediction As A Service
Amartya Sanyal;Matt Kusner;Adria Gascon;Varun Kanade;
Geodesic Convolutional Shape Optimization
Pierre Baque;Edoardo Remelli;Francois Fleuret;EPFL Pascal Fua;
An Estimation And Analysis Framework For The Rasch Model
Andrew Lan;Mung Chiang;Christoph Studer;
AutoPrognosis: Automated Clinical Prognostic Modeling Via Bayesian Optimization With Structured Kernel Learning
Ahmed M. Alaa Ibrahim;M van der Schaar;
Limits Of Estimating Heterogeneous Treatment Effects: Guidelines For Practical Algorithm Design
Ahmed M. Alaa Ibrahim;M van der Schaar;
End-to-end Active Object Tracking Via Reinforcement Learning
Wenhan Luo;Peng Sun;Fangwei Zhong;Wei Liu;Tong Zhang;Yizhou Wang;


Other Models and Methods


   Paper Name    Author
Learning Equations For Extrapolation And Control
Subham S Sahoo;Christoph Lampert;Georg Martius;
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen;Udit Gupta;Bob Adolf;Michael Mitzenmacher;Alexander Rush;Gu-Yeon Wei;David Brooks;
Transformation Autoregressive Networks
Junier Oliva;Avinava Dubey;Manzil Zaheer;BarnabȢs PȮczos;Russ Salakhutdinov;Eric Xing;Jeff Schneider;
Interpretability Beyond Feature Attribution: Quantitative Testing With Concept Activation Vectors (TCAV)
Been Kim;Martin Wattenberg;Justin Gilmer;Carrie Cai;James Wexler;Fernanda ViȦgas;Rory sayres;
PDE-Net: Learning PDEs From Data
Zichao Long;Yiping Lu;Xianzhong Ma;Bin Dong;


Parallel and Distributed Learning


   Paper Name    Author
Coded Sparse Matrix Multiplication
Sinong Wang;Jiashang Liu;Ness Shroff;
Error Compensated Quantized SGD And Its Applications To Large-scale Distributed Optimization
Jiaxiang Wu;Weidong Huang;Junzhou Huang;Tong Zhang;
Faster Derivative-Free Stochastic Algorithm For Shared Memory Machines
Bin Gu;Zhouyuan Huo;Cheng Deng;Heng Huang;
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence And Sparse Communication
Zebang Shen;Aryan Mokhtari;Tengfei Zhou;Peilin Zhao;Hui Qian;
Optimal Tuning For Divide-and-conquer Kernel Ridge Regression With Massive Data
Ganggang Xu;Zuofeng Shang;Guang Cheng;
Exploring Hidden Dimensions In Accelerating Convolutional Neural Networks
Zhihao Jia;Sina Lin;Charles Qi;Alex Aiken;
DICOD: Distributed Convolutional Coordinate Descent For Convolutional Sparse Coding
Thomas Moreau;Laurent Oudre;Nicolas Vayatis;
Distributed Asynchronous Optimization With Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou;Panayotis Mertikopoulos;Nicholas Bambos;Peter Glynn;Yinyu Ye;Li-Jia Li;Li Fei-Fei;
Distributed Nonparametric Regression Under Communication Constraints
Yuancheng Zhu;John Lafferty;


Poster Sessions


   Paper Name    Author
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik;Andy Palaniappan;Jaime Fisac;Dylan Hadfield-Menell;Stuart Russell;EECS Anca Dragan;
A Theoretical Explanation For Perplexing Behaviors Of Backpropagation-based Visualizations
Weili Nie;Yang Zhang;Ankit Patel;
Communication-Computation Efficient Gradient Coding
Min Ye;Emmanuel Abbe;
The Power Of Interpolation: Understanding The Effectiveness Of SGD In Modern Over-parametrized Learning
Siyuan Ma;Raef Bassily;Mikhail Belkin;
Not All Samples Are Created Equal: Deep Learning With Importance Sampling
Angelos Katharopoulos;Francois Fleuret;
Fast Bellman Updates For Robust MDPs
Clint Ho;Marek Petrik;Wolfram Wiesemann;
Local Density Estimation In High Dimensions
Xian Wu;Moses Charikar;Vishnu Natchu;
Fast Variance Reduction Method With Stochastic Batch Size
University of California Xuanqing Liu;Cho-Jui Hsieh;
Analyzing The Robustness Of Nearest Neighbors To Adversarial Examples
Yizhen Wang;Somesh Jha;Kamalika Chaudhuri;
NetGAN: Generating Graphs Via Random Walks
Aleksandar Bojchevski;Oleksandr Shchur;Daniel Zȹgner;Stephan Gȹnnemann;
Discovering And Removing Exogenous State Variables And Rewards For Reinforcement Learning
Thomas Dietterich;George Trimponias;Zhitang Chen;
On The Generalization Of Equivariance And Convolution In Neural Networks To The Action Of Compact Groups
Risi Kondor;Shubhendu Trivedi;
Stochastic Wasserstein Barycenters
Sebastian Claici;Edward Chien;Justin Solomon;
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama;Dave Zachariah;Thomas Sch?n;
PIPPS: Flexible Model-Based Policy Search Robust To The Curse Of Chaos
Paavo Parmas;Carl E Rasmussen;Jan Peters;Kenji Doya;
Born Again Neural Networks
Tommaso Furlanello;Zachary Lipton;Michael Tschannen;Laurent Itti;Anima Anandkumar;
Provable Defenses Against Adversarial Examples Via The Convex Outer Adversarial Polytope
Eric Wong;Zico Kolter;
Inference Suboptimality In Variational Autoencoders
Chris Cremer;Xuechen Li;David Duvenaud;
Comparison-Based Random Forests
Siavash Haghiri;Damien Garreau;Ulrike von Luxburg;
Deep Variational Reinforcement Learning For POMDPs
Maximilian Igl;Luisa Zintgraf;Tuan Anh Le;Frank Wood;Shimon Whiteson;
Gradient Descent For Sparse Rank-One Matrix Completion For Crowd-Sourced Aggregation Of Sparsely Interacting Workers
Yao Ma;Alexander Olshevsky;Csaba Szepesvari;Venkatesh Saligrama;
Composable Planning With Attributes
Amy Zhang;Sainbayar Sukhbaatar;Adam Lerer;Arthur Szlam;Facebook Rob Fergus;
Bounds On The Approximation Power Of Feedforward Neural Networks
Mohammad Mehrabi;Aslan Tchamkerten;MANSOOR I YOUSEFI;
Been There, Done That: Meta-Learning With Episodic Recall
Sam Ritter;Jane Wang;Zeb Kurth-Nelson;Siddhant Jayakumar;Charles Blundell;Razvan Pascanu;Matthew Botvinick;
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation And Theoretical Analysis
Pengtao Xie;Wei Wu;Yichen Zhu;Eric Xing;
Augment And Reduce: Stochastic Inference For Large Categorical Distributions
Francisco Ruiz;Michalis Titsias;Adji Bousso Dieng;David Blei;
Low-Rank Riemannian Optimization On Positive Semidefinite Stochastic Matrices With Applications To Graph Clustering
Ahmed Douik;Babak Hassibi;
Weakly Consistent Optimal Pricing Algorithms In Repeated Posted-Price Auctions With Strategic Buyer
Alexey Drutsa;
Bounding And Counting Linear Regions Of Deep Neural Networks
Thiago Serra;Christian Tjandraatmadja;Srikumar Ramalingam;
Noisin: Unbiased Regularization For Recurrent Neural Networks
Adji Bousso Dieng;Rajesh Ranganath;Jaan Altosaar;David Blei;
Recurrent Predictive State Policy Networks
Ahmed Hefny;Zita Marinho;Wen Sun;Siddhartha Srinivasa;Geoff Gordon;
Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann;Ricardo Cerri;Rodrigo Barros;
Binary Partitions With Approximate Minimum Impurity
Eduardo Laber;Marco Molinaro;Felipe de A. Mello Pereira;
Detecting Non-causal Artifacts In Multivariate Linear Regression Models
Dominik Janzing;Bernhard Sch?lkopf;
The Hidden Vulnerability Of Distributed Learning In Byzantium
El Mahdi El Mhamdi;Rachid Guerraoui;SȦbastien Rouault;
Fully Decentralized Multi-Agent Reinforcement Learning With Networked Agents
Kaiqing Zhang;Zhuoran Yang;Han Liu;Tong Zhang;Tamer Basar;
Orthogonal Machine Learning: Power And Limitations
Ilias Zadik;Lester Mackey;Vasilis Syrgkanis;
Modeling Sparse Deviations For Compressed Sensing Using Generative Models
Manik Dhar;Aditya Grover;Stefano Ermon;
Celer: A Fast Solver For The Lasso With Dual Extrapolation
Mathurin MASSIAS;Joseph Salmon;Alexandre Gramfort;
Synthesizing Programs For Images Using Reinforced Adversarial Learning
Yaroslav Ganin;Tejas Kulkarni;Igor Babuschkin;S. M. Ali Eslami;Oriol Vinyals;
Inter And Intra Topic Structure Learning With Word Embeddings
He Zhao;Lan Du;Wray Buntine;Mingyuan Zhou;
Machine Theory Of Mind
Neil Rabinowitz;Frank Perbet;Francis Song;Chiyuan Zhang;S. M. Ali Eslami;Matthew Botvinick;
Continual Reinforcement Learning With Complex Synapses
Christos Kaplanis;Murray Shanahan;Claudia Clopath;
Convolutional Imputation Of Matrix Networks
Qingyun Sun;Mengyuan Yan;David Donoho;stephen boyd;
Unbiased Objective Estimation In Predictive Optimization
Shinji Ito;Akihiro Yabe;Ryohei Fujimaki;
Stein Points
Wilson Ye Chen;Lester Mackey;Jackson Gorham;Francois-Xavier Briol;Chris J Oates;
Neural Autoregressive Flows
Chin-Wei Huang;David Krueger;Alexandre Lacoste;Aaron Courville;
Active Learning With Logged Data
Songbai Yan;Kamalika Chaudhuri;Tara Javidi;
Explicit Inductive Bias For Transfer Learning With Convolutional Networks
Xuhong LI;Yves Grandvalet;Franck Davoine;
Least-Squares Temporal Difference Learning For The Linear Quadratic Regulator
Stephen Tu;Benjamin Recht;
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa;Chenyang Tao;Chunyuan Li;Courtney Page;Benjamin Goldstein;Lawrence Carin;Ricardo Henao;
Asynchronous Byzantine Machine Learning (the Case Of SGD)
Georgios Damaskinos;El Mahdi El Mhamdi;Rachid Guerraoui;Rhicheek Patra;Mahsa Taziki;
Nonconvex Optimization For Regression With Fairness Constraints
Junpei Komiyama;Akiko Takeda;Junya Honda;Hajime Shimao;
SAFFRON: An Adaptive Algorithm For Online Control Of The False Discovery Rate
Aaditya Ramdas;Tijana Zrnic;Martin Wainwright;Michael Jordan;
On The Implicit Bias Of Dropout
Poorya Mianjy;Raman Arora;Rene Vidal;
Fairness Without Demographics In Repeated Loss Minimization
Tatsunori Hashimoto;Megha Srivastava;Hongseok Namkoong;Percy Liang;
Adversarial Risk And The Dangers Of Evaluating Against Weak Attacks
Jonathan Uesato;Brendan O'Donoghue;Pushmeet Kohli;A?ron van den Oord;
ContextNet: Deep Learning For Star Galaxy Classification
Noble Kennamer;University of California David Kirkby;Alex Ihler;University of California Francisco Javier Sanchez-Lopez;
Goodness-of-fit Testing For Discrete Distributions Via Stein Discrepancy
Jiasen Yang;Qiang Liu;Vinayak A Rao;Jennifer Neville;
Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang;Rui Luo;M. Li;Ming Zhou;Weinan Zhang;Jun Wang;
The Uncertainty Bellman Equation And Exploration
Brendan O'Donoghue;Ian Osband;Remi Munos;Vlad Mnih;
GradNorm: Gradient Normalization For Adaptive Loss Balancing In Deep Multitask Networks
Zhao Chen;Vijay Badrinarayanan;Chen-Yu Lee;Andrew Rabinovich;
CRVI: Convex Relaxation For Variational Inference
Ghazal Fazelnia;John Paisley;
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo;Chang Liu;Jiaxin Shi;Jun Zhu;Ning Chen;Bo Zhang;
Reinforcing Adversarial Robustness Using Model Confidence Induced By Adversarial Training
Xi Wu;Uyeong Jang;Jiefeng Chen;Lingjiao Chen;Somesh Jha;
Bayesian Uncertainty Estimation For Batch Normalized Deep Networks
Mattias Teye;Hossein Azizpour;Kevin Smith;
Efficient Neural Architecture Search Via Parameters Sharing
Hieu Pham;Melody Guan;Barret Zoph;Quoc Le;Jeff Dean;
Spatio-temporal Bayesian On-line Changepoint Detection With Model Selection
Jeremias Knoblauch;Theo Damoulas;
Proportional Allocation: Simple, Distributed, And Diverse Matching With High Entropy
Shipra Agarwal;Morteza Zadimoghaddam;Vahab Mirrokni;
Autoregressive Convolutional Neural Networks For Asynchronous Time Series
Mikolaj Binkowski;Gautier Marti;Philippe Donnat;
Cut-Pursuit Algorithm For Regularizing Nonsmooth Functionals With Graph Total Variation
Hugo Raguet;loic landrieu;
Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang;Chao Du;Jun Zhu;
Massively Parallel Algorithms And Hardness For Single-Linkage Clustering Under $\ell_p$ Distances
Grigory Yaroslavtsev;Adithya Vadapalli;
GraphRNN: Generating Realistic Graphs With Deep Auto-regressive Models
Jiaxuan You;Zhitao Ying;Xiang Ren;Will Hamilton;Jure Leskovec;
Towards Black-box Iterative Machine Teaching
Weiyang Liu;Bo Dai;Xingguo Li;Zhen Liu;Jim Rehg;Le Song;
Knowledge Transfer With Jacobian Matching
Suraj Srinivas;Francois Fleuret;
Regret Minimization For Partially Observable Deep Reinforcement Learning
Peter Jin;EECS Kurt Keutzer;Sergey Levine;
Accurate Inference For Adaptive Linear Models
Yash Deshpande;Lester Mackey;Vasilis Syrgkanis;Matt Taddy;
Open Category Detection With PAC Guarantees
Si Liu;Risheek Garrepalli;Thomas Dietterich;Alan Fern;Dan Hendrycks;
An Algorithmic Framework Of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
Li Shen;Peng Sun;Yitong Wang;Wei Liu;Tong Zhang;
Neural Relational Inference For Interacting Systems
Thomas Kipf;Ethan Fetaya;Jackson Wang;Max Welling;Richard Zemel;
Black-Box Variational Inference For Stochastic Differential Equations
Tom Ryder;Andrew Golightly;Stephen McGough;Dennis Prangle;
Dependent Relational Gamma Process Models For Longitudinal Networks
Sikun Yang;Heinz Koeppl;
An Optimal Control Approach To Deep Learning And Applications To Discrete-Weight Neural Networks
Qianxiao Li;IHPC Shuji Hao;
Reinforcement Learning With Function-Valued Action Spaces For Partial Differential Equation Control
Yangchen Pan;Amir-massoud Farahmand;Martha White;Saleh Nabi;Piyush Grover;Daniel Nikovski;
Partial Optimality And Fast Lower Bounds For Weighted Correlation Clustering
Jan-Hendrik Lange;Andreas Karrenbauer;Bjoern Andres;
Bayesian Coreset Construction Via Greedy Iterative Geodesic Ascent
Trevor Campbell;Tamara Broderick;
Synthesizing Robust Adversarial Examples
Anish Athalye;Logan Engstrom;Andrew Ilyas;Kevin Kwok;
Pathwise Derivatives Beyond The Reparameterization Trick
Martin Jankowiak;Fritz Obermeyer;
Distilling The Posterior In Bayesian Neural Networks
Jackson Wang;Paul Vicol;James Lucas;Li Gu;Roger Grosse;Richard Zemel;
StrassenNets: Deep Learning With A Multiplication Budget
Michael Tschannen;Aran Khanna;Animashree Anandkumar;
Learning Compact Neural Networks With Regularization
Samet Oymak;
Minimal I-MAP MCMC For Scalable Structure Discovery In Causal DAG Models
Raj Agrawal;Caroline Uhler;Tamara Broderick;
Classification From Pairwise Similarity And Unlabeled Data
Han Bao;Gang Niu;Masashi Sugiyama;
Nonparametric Variable Importance Using An Augmented Neural Network With Multi-task Learning
Jean Feng;Brian Williamson;Noah Simon;Marco Carone;
Measuring Abstract Reasoning In Neural Networks
Adam Santoro;Feilx Hill;David GT Barrett;Ari S Morcos;Tim Lillicrap;
Fast And Sample Efficient Inductive Matrix Completion Via Multi-Phase Procrustes Flow
Xiao Zhang;Simon Du;Quanquan Gu;
Stabilizing Gradients For Deep Neural Networks Via Efficient SVD Parameterization
Jiong Zhang;Qi Lei;Inderjit Dhillon;
Noisy Natural Gradient As Variational Inference
Guodong Zhang;Shengyang Sun;David Duvenaud;Roger Grosse;
A Probabilistic Theory Of Supervised Similarity Learning For Pointwise ROC Curve Optimization
Robin Vogel;AurȦlien Bellet;StȦphan ClȦmen?on;
Representation Tradeoffs For Hyperbolic Embeddings
Frederic Sala;Chris De Sa;Albert Gu;Christopher Re;
DCFNet: Deep Neural Network With Decomposed Convolutional Filters
Qiang Qiu;Xiuyuan Cheng;robert Calderbank;Guillermo Sapiro;
Self-Bounded Prediction Suffix Tree Via Approximate String Matching
Dongwoo Kim;Christian Walder;
Fair And Diverse DPP-Based Data Summarization
Elisa Celis;Vijay Keswani;Damian Straszak;Amit Jayant Deshpande;Tarun Kathuria;Nisheeth Vishnoi;
Deep K-Means: Re-Training And Parameter Sharing With Harder Cluster Assignments For Compressing Deep Convolutions
Junru Wu;Yue Wang;Zhenyu Wu;Zhangyang Wang;Ashok Veeraraghavan;Yingyan Lin;
A Simple Stochastic Variance Reduced Algorithm With Fast Convergence Rates
Kaiwen Zhou;Fanhua Shang;James Cheng;
WSNet: Compact And Efficient Networks Through Weight Sampling
Xiaojie Jin;Yingzhen Yang;Ning Xu;Jianchao Yang;Nebojsa Jojic;Jiashi Feng;Shuicheng Yan;
Essentially No Barriers In Neural Network Energy Landscape
Felix Draxler;Kambis Veschgini;Manfred Salmhofer;Fred Hamprecht;
Hierarchical Deep Generative Models For Multi-Rate Multivariate Time Series
Zhengping Che;Sanjay Purushotham;Max Guangyu Li;Bo Jiang;Yan Liu;
Decoupling Gradient-Like Learning Rules From Representations
Philip Thomas;Christoph Dann;Emma Brunskill;
A Unified Framework For Structured Low-rank Matrix Learning
Pratik Kumar Jawanpuria;Bamdev Mishra;
Learning Deep ResNet Blocks Sequentially Using Boosting Theory
Furong Huang;Jordan Ash;John Langford;Robert Schapire;
Efficient Bias-Span-Constrained Exploration-Exploitation In Reinforcement Learning
Ronan Fruit;Matteo Pirotta;Alessandro Lazaric;Ronald Ortner;
Decoupled Parallel Backpropagation With Convergence Guarantee
Zhouyuan Huo;Bin Gu;Qian Yang;Heng Huang;
Efficient First-Order Algorithms For Adaptive Signal Denoising
Dmitrii Ostrovskii;Zaid Harchaoui;
SGD And Hogwild! Convergence Without The Bounded Gradients Assumption
Lam Nguyen;PHUONG HA NGUYEN;Marten van Dijk;Peter Richtarik;Katya Scheinberg;Martin Takac;
DRACO: Byzantine-resilient Distributed Training Via Redundant Gradients
Lingjiao Chen;Hongyi Wang;Zachary Charles;Dimitris Papailiopoulos;
Improved Regret Bounds For Thompson Sampling In Linear Quadratic Control Problems
Marc Abeille;Alessandro Lazaric;
MAGAN: Aligning Biological Manifolds
Matt Amodio;Smita Krishnaswamy;
Riemannian Stochastic Recursive Gradient Algorithm With Retraction And Vector Transport And Its Convergence Analysis
Hiroyuki Kasai;Hiroyuki Sato;Bamdev Mishra;
Learning To Optimize Combinatorial Functions
Nir Rosenfeld;Eric Balkanski;Amir Globerson;Yaron Singer;
Improving Sign Random Projections With Additional Information
Keegan Kang;Wei Pin Wong;
Delayed Impact Of Fair Machine Learning
Lydia T. Liu;Sarah Dean;Esther Rolf;Max Simchowitz;University of California Moritz Hardt;
Yes, But Did It Work?: Evaluating Variational Inference
Yuling Yao;Aki Vehtari;Daniel Simpson;Andrew Gelman;
Differentiable Abstract Interpretation For Provably Robust Neural Networks
Matthew Mirman;Timon Gehr;Martin Vechev;
Comparing Dynamics: Deep Neural Networks Versus Glassy Systems
Marco Baity-Jesi;Levent Sagun;Mario Geiger;Stefano Spigler;Gerard Arous;Chiara Cammarota;Yann LeCun;Matthieu Wyart;Giulio Biroli;
Letos Be Honest: An Optimal No-Regret Framework For Zero-Sum Games
Ehsan Asadi Kangarshahi;Ya-Ping Hsieh;Mehmet Fatih Sahin;Volkan Cevher;
Graphical Nonconvex Optimization Via An Adaptive Convex Relaxation
Qiang Sun;Kean Ming Tan;Han Liu;Tong Zhang;
Transfer In Deep Reinforcement Learning Using Successor Features And Generalised Policy Improvement
Andre Barreto;Diana Borsa;John Quan;Tom Schaul;David Silver;Matteo Hessel;Daniel J. Mankowitz;Augustin Zidek;Remi Munos;
Efficient Model-Based Deep Reinforcement Learning With Variational State Tabulation
Dane Corneil;Wulfram Gerstner;Johanni Brea;
Oi-VAE: Output Interpretable VAEs For Nonlinear Group Factor Analysis
Samuel Ainsworth;Nick J Foti;Adrian KC Lee;Emily Fox;
Lightweight Stochastic Optimization For Minimizing Finite Sums With Infinite Data
Shuai Zheng;James Kwok;
Spline Filters For End-to-End Deep Learning
Randall Balestriero;Romain Cosentino;Herve Glotin;Richard Baraniuk;
Path Consistency Learning In Tsallis Entropy Regularized MDPs
Yinlam Chow;Ofir Nachum;Mohammad Ghavamzadeh;


Privacy, Anonymity, and Security




Ranking and Preference Learning


   Paper Name    Author
Extreme Learning To Rank Via Low Rank Assumption
Minhao Cheng;Ian Davidson;Cho-Jui Hsieh;
SQL-Rank: A Listwise Approach To Collaborative Ranking
LIWEI WU;Cho-Jui Hsieh;University of California James Sharpnack;
Ranking Distributions Based On Noisy Sorting
Adil El Mesaoudi-Paul;Eyke Hȹllermeier;Robert Busa-Fekete;
Parameterized Algorithms For The Matrix Completion Problem
Robert Ganian;DePaul Iyad Kanj;Sebastian Ordyniak;Stefan Szeider;
The Limits Of Maxing, Ranking, And Preference Learning
Moein Falahatgar;Ayush Jain;Alon Orlitsky;Venkatadheeraj Pichapati;Vaishakh Ravindrakumar;
Accelerated Spectral Ranking
Arpit Agarwal;Prathamesh Patil;Shivani Agarwal;
The Weighted Kendall And High-order Kernels For Permutations
Yunlong Jiao;JP Vert;
Composite Marginal Likelihood Methods For Random Utility Models
Zhibing Zhao;Lirong Xia;
Learning A Mixture Of Two Multinomial Logits
Flavio Chierichetti;Ravi Kumar;Andrew Tomkins;


Reinforcement Learning


   Paper Name    Author
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure In MDPs
Andrea Zanette;Emma Brunskill;
Hierarchical Imitation And Reinforcement Learning
Hoang M Le;Nan Jiang;Alekh Agarwal;Miroslav Dudik;Yisong Yue;Hal Daume;
Global Convergence Of Policy Gradient Methods For The Linear Quadratic Regulator
Maryam Fazel;Rong Ge;Sham Kakade;Mehran Mesbahi;
Time Limits In Reinforcement Learning
Fabio Pardo;Arash Tavakoli;Vitaly Levdik;Petar Kormushev;
Policy Optimization As Wasserstein Gradient Flows
RUIYI ZHANG;Changyou Chen;Chunyuan Li;Lawrence Carin;
Structured Evolution With Compact Architectures For Scalable Policy Optimization
Krzysztof Choromanski;Mark Rowland;Vikas Sindhwani;Richard E Turner;Adrian Weller;
An Inference-Based Policy Gradient Method For Learning Options
Matthew Smith;Herke van Hoof;Joelle Pineau;
Learning By Playing - Solving Sparse Reward Tasks From Scratch
Martin Riedmiller;Roland Hafner;Thomas Lampe;Michael Neunert;Jonas Degrave;Tom Van de Wiele;Vlad Mnih;Nicolas Heess;Jost Springenberg;
GEP-PG: Decoupling Exploration And Exploitation In Deep Reinforcement Learning Algorithms
CȦdric Colas;Olivier Sigaud;Pierre-Yves Oudeyer;
Gated Path Planning Networks
Lisa Lee;Emilio Parisotto;Devendra Singh Chaplot;Eric Xing;Russ Salakhutdinov;
Configurable Markov Decision Processes
Alberto Maria Metelli;Mirco Mutti;Marcello Restelli;
Smoothed Action Value Functions For Learning Gaussian Policies
Ofir Nachum;Mohammad Norouzi;George Tucker;Dale Schuurmans;
Visualizing And Understanding Atari Agents
Samuel Greydanus;Anurag Koul;Jonathan Dodge;Alan Fern;
Fourier Policy Gradients
Matthew Fellows;Kamil Ciosek;Shimon Whiteson;
IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures
Lasse Espeholt;Hubert Soyer;Remi Munos;Karen Simonyan;Vlad Mnih;Tom Ward;Yotam Doron;Vlad Firoiu;Tim Harley;Iain Dunning;Shane Legg;koray kavukcuoglu;
Coordinated Exploration In Concurrent Reinforcement Learning
Maria Dimakopoulou;Benjamin Van Roy;
Investigating Human Priors For Playing Video Games
Rachit Dubey;Pulkit Agrawal;Deepak Pathak;Tom Griffiths;Alexei Efros;
RLlib: Abstractions For Distributed Reinforcement Learning
Eric Liang;Richard Liaw;Robert Nishihara;Philipp Moritz;Roy Fox;Ken Goldberg;Joseph Gonzalez;Michael Jordan;Ion Stoica;
Importance Weighted Transfer Of Samples In Reinforcement Learning
Andrea Tirinzoni;Andrea Sessa;Matteo Pirotta;Marcello Restelli;
State Abstractions For Lifelong Reinforcement Learning
David Abel;Dilip S. Arumugam;Lucas Lehnert;Michael L. Littman;
Universal Planning Networks: Learning Generalizable Representations For Visuomotor Control
Aravind Srinivas;Allan Jabri;Pieter Abbeel;Sergey Levine;Chelsea Finn;
Addressing Function Approximation Error In Actor-Critic Methods
Scott Fujimoto;Herke van Hoof;David Meger;
SBEED: Convergent Reinforcement Learning With Nonlinear Function Approximation
Bo Dai;Albert Shaw;Lihong Li;Lin Xiao;Niao He;Zhen Liu;Jianshu Chen;Le Song;
Policy And Value Transfer In Lifelong Reinforcement Learning
David Abel;Yuu Jinnai;Sophie Guo;George Konidaris;Michael L. Littman;
Deep Reinforcement Learning In Continuous Action Spaces: A Case Study In The Game Of Simulated Curling
kyowoon Lee;Sol-A Kim;Jaesik Choi;Seong-Whan Lee;
Implicit Quantile Networks For Distributional Reinforcement Learning
Will Dabney;Georg Ostrovski;David Silver;Remi Munos;
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu;Alex Irpan;Jacob Andreas;Bobby Kleinberg;Quoc Le;Jon Kleinberg;
Best Arm Identification In Linear Bandits With Linear Dimension Dependency
Chao Tao;SaȲl A. Blanco;Yuan Zhou;
Mix & Match - Agent Curricula For Reinforcement Learning
Wojciech Czarnecki;Siddhant Jayakumar;Max Jaderberg;Leonard Hasenclever;Yee Teh;Nicolas Heess;Simon Osindero;Razvan Pascanu;
Lipschitz Continuity In Model-based Reinforcement Learning
Kavosh Asadi;Dipendra Misra;Michael L. Littman;
Competitive Multi-agent Inverse Reinforcement Learning With Sub-optimal Demonstrations
IEMS Xingyu Wang;Diego Klabjan;
Clipped Action Policy Gradient
Yasuhiro Fujita;Shin-ichi Maeda;
Learning To Explore Via Meta-Policy Gradient
Tianbing Xu;Qiang Liu;Liang Zhao;Jian Peng;
Self-Imitation Learning
Junhyuk Oh;Yijie Guo;Satinder Singh;Honglak Lee;
Beyond The One-Step Greedy Approach In Reinforcement Learning
Yonathan Efroni;Gal Dalal;Bruno Scherrer;Shie Mannor;
Latent Space Policies For Hierarchical Reinforcement Learning
Tuomas Haarnoja;Kristian Hartikainen;Pieter Abbeel;Sergey Levine;
The Mirage Of Action-Dependent Baselines In Reinforcement Learning
George Tucker;Surya Bhupatiraju;Shixiang Gu;Richard E Turner;Zoubin Ghahramani;Sergey Levine;
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar;Yinlam Chow;Mohammad Ghavamzadeh;
Automatic Goal Generation For Reinforcement Learning Agents
Carlos Florensa;David Held;Xinyang Geng;Pieter Abbeel;
Feedback-Based Tree Search For Reinforcement Learning
Daniel Jiang;Emmanuel Ekwedike;Han Liu;
Using Reward Machines For High-Level Task Specification And Decomposition In Reinforcement Learning
Rodrigo A Toro Icarte;Toryn Q Klassen;Richard Valenzano;Sheila McIlraith;
Programmatically Interpretable Reinforcement Learning
Abhinav Verma;Vijayaraghavan Murali;Rishabh Singh;Pushmeet Kohli;Swarat Chaudhuri;
Scalable Bilinear Pi Learning Using State And Action Features
Yichen Chen;Lihong Li;Mengdi Wang;
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning With A Stochastic Actor
Tuomas Haarnoja;Aurick Zhou;Pieter Abbeel;Sergey Levine;
Convergent Tree Backup And Retrace With Function Approximation
Ahmed Touati;Pierre-Luc Bacon;Doina Precup;Pascal Vincent;
Learning With Abandonment
Sven Schmit;Ramesh Johari;
Spotlight: Optimizing Device Placement For Training Deep Neural Networks
Yuanxiang Gao;Department of Electrical and Computer Li Chen;Baochun Li;
Structured Control Nets For Deep Reinforcement Learning
Mario Srouji;Jian Zhang;Russ Salakhutdinov;
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning With Trajectory Embeddings
JD Co-Reyes;Yu Xuan Liu;Abhishek Gupta;Benjamin Eysenbach;Pieter Abbeel;Sergey Levine;
Learning The Reward Function For A Misspecified Model
Erik Talvitie;


Representation Learning


   Paper Name    Author
SignSGD: Compressed Optimisation For Non-Convex Problems
Jeremy Bernstein;Yu-Xiang Wang;Kamyar Azizzadenesheli;Anima Anandkumar;
Learning K-way D-dimensional Discrete Codes For Compact Embedding Representations
Ting Chen;Martin Reqiang Min;Yizhou Sun;
Learning Independent Causal Mechanisms
Giambattista Parascandolo;Niki Kilbertus;Mateo Rojas-Carulla;Bernhard Sch?lkopf;
Improving Optimization In Models With Continuous Symmetry Breaking
Robert Bamler;Stephan Mandt;
A Probabilistic Framework For Multi-view Feature Learning With Many-to-many Associations Via Neural Networks
Akifumi Okuno;Tetsuya Hada;Hidetoshi Shimodaira;
CoVeR: Learning Covariate-Specific Vector Representations With Tensor Decompositions
Kevin Tian;Teng Zhang;James Zou;
Anonymous Walk Embeddings
Sergey Ivanov;Evgeny Burnaev;
Contextual Graph Markov Model: A Deep And Generative Approach To Graph Processing
Davide Bacciu;Federico Errica;Alessio Micheli;
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian;Wei Zhang;Ce Zhang;Ji Liu;
Disentangling By Factorising
Hyunjik Kim;Andriy Mnih;
Tree Edit Distance Learning Via Adaptive Symbol Embeddings
Benjamin Paa?en;Claudio Gallicchio;Alessio Micheli;CITEC Barbara Hammer;
Hyperbolic Entailment Cones For Learning Hierarchical Embeddings
Octavian-Eugen Ganea;Gary Becigneul;Thomas Hofmann;
Learning Continuous Hierarchies In The Lorentz Model Of Hyperbolic Geometry
Maximillian Nickel;Douwe Kiela;
Learning Steady-States Of Iterative Algorithms Over Graphs
Hanjun Dai;Zornitsa Kozareva;Bo Dai;Alex Smola;Le Song;
Discovering Interpretable Representations For Both Deep Generative And Discriminative Models
Tameem Adel;Zoubin Ghahramani;Adrian Weller;
Katyusha X: Simple Momentum Method For Stochastic Sum-of-Nonconvex Optimization
Zeyuan Allen-Zhu;
Generative Temporal Models With Spatial Memory For Partially Observed Environments
Marco Fraccaro;Danilo J. Rezende;Yori Zwols;Alexander Pritzel;S. M. Ali Eslami;Fabio Viola;


Society Impacts of Machine Learning


   Paper Name    Author
A Reductions Approach To Fair Classification
Alekh Agarwal;Alina Beygelzimer;Miroslav Dudik;John Langford;Hanna Wallach;
Blind Justice: Fairness With Encrypted Sensitive Attributes
Niki Kilbertus;Adria Gascon;Matt Kusner;Michael Veale;Krishna Gummadi;Adrian Weller;
Preventing Fairness Gerrymandering: Auditing And Learning For Subgroup Fairness
Michael Kearns;Seth V Neel;Aaron Roth;Zhiwei Wu;
Probably Approximately Metric-Fair Learning
Gal Yona;Guy Rothblum;


Sparsity and Compressed Sensing


   Paper Name    Author
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy;Namrata Vaswani;
Safe Element Screening For Submodular Function Minimization
Weizhong Zhang;Bin Hong;Lin Ma;Wei Liu;Tong Zhang;
Signal And Noise Statistics Oblivious Orthogonal Matching Pursuit
Sreejith Kallummil;Sheetal Kalyani;
Covariate Adjusted Precision Matrix Estimation Via Nonconvex Optimization
Jinghui Chen;Pan Xu;Lingxiao Wang;Jian Ma;Quanquan Gu;
Linear Spectral Estimators And An Application To Phase Retrieval
Ramina Ghods;Andrew Lan;Tom Goldstein;Christoph Studer;
Online Convolutional Sparse Coding With Sample-Dependent Dictionary
Yaqing WANG;Quanming Yao;James Kwok;Lionel NI;
Testing Sparsity Over Known And Unknown Bases
Siddharth Barman;Arnab Bhattacharyya;Suprovat Ghoshal;
WHInter: A Working Set Algorithm For High-dimensional Sparse Second Order Interaction Models
Marine LE MORVAN;JP Vert;


Spectral Methods




Statistical Learning Theory




Structured Prediction


   Paper Name    Author
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae;Andre Filipe Torres Martins;Mathieu Blondel;Claire Cardie;
Learning To Speed Up Structured Output Prediction
Xingyuan Pan;Vivek Srikumar;
Differentiable Dynamic Programming For Structured Prediction And Attention
Arthur Mensch;Mathieu Blondel;
Structured Output Learning With Abstention: Application To Accurate Opinion Prediction
Alexandre Garcia;Telecom-ParisTech ChloȦ Clavel;Slim Essid;Florence d'Alche-Buc;
Predict And Constrain: Modeling Cardinality In Deep Structured Prediction
Nataly Brukhim;Amir Globerson;
End-to-End Learning For The Deep Multivariate Probit Model
Di Chen;Yexiang Xue;Carla Gomes;
Learning Maximum-A-Posteriori Perturbation Models For Structured Prediction In Polynomial Time
Asish Ghoshal;Jean Honorio;
Efficient And Consistent Adversarial Bipartite Matching
Rizal Fathony;Sima Behpour;Xinhua Zhang;Brian Ziebart;


Supervised Learning


   Paper Name    Author
Learning To Reweight Examples For Robust Deep Learning
Mengye Ren;Wenyuan Zeng;Bin Yang;Raquel Urtasun;
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin;Yudong Chen;Kannan Ramchandran;Peter Bartlett;
Finding Influential Training Samples For Gradient Boosted Decision Trees
Boris Sharchilev;Yury Ustinovskiy;Pavel Serdyukov;Maarten de Rijke;
Curriculum Learning By Transfer Learning: Theory And Experiments With Deep Networks
Daphna Weinshall;Gad A Cohen;Dan Amir;
Candidates Vs. Noises Estimation For Large Multi-Class Classification Problem
Lei Han;Yiheng Huang;Tong Zhang;
Improving Regression Performance With Distributional Losses
Ehsan Imani;Martha White;
Functional Gradient Boosting Based On Residual Network Perception
Atsushi Nitanda;Taiji Suzuki;
MentorNet: Learning Data-Driven Curriculum For Very Deep Neural Networks On Corrupted Labels
Lu Jiang;Zhengyuan Zhou;Thomas Leung;Li-Jia Li;Li Fei-Fei;
Optimal Distributed Learning With Multi-pass Stochastic Gradient Methods
Junhong Lin;Volkan Cevher;
Attention-based Deep Multiple Instance Learning
Maximilian Ilse;Jakub Tomczak;Max Welling;
Prediction Rule Reshaping
Matt Bonakdarpour;Sabyasachi Chatterjee;Rina Barber;John Lafferty;
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu;Gang Niu;Issei Sato;Masashi Sugiyama;
Noise2Noise: Learning Image Restoration Without Clean Data
Jaakko Lehtinen;Jacob Munkberg;Jon Hasselgren;Samuli Laine;Tero Karras;Miika Aittala;Timo Aila;
Learning And Memorization
Sat Chatterjee;
Binary Classification With Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan;Sanmi Koyejo;Kai Zhong;Pradeep Ravikumar;
Inductive Two-Layer Modeling With Parametric Bregman Transfer
Vignesh Ganapathiraman;Zhan Shi;Xinhua Zhang;Yaoliang Yu;
CRAFTML, An Efficient Clustering-based Random Forest For Extreme Multi-label Learning
Wissam Siblini;Frank Meyer;Pascale Kuntz;
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings
Aviral Kumar;Sunita Sarawagi;Ujjwal Jain;
Dimensionality-Driven Learning With Noisy Labels
Daniel Ma;Yisen Wang;Michael E. Houle;Shuo Zhou;Sarah Erfani;Shutao Xia;Sudanthi Wijewickrema;James Bailey;


Time-Series Analysis


   Paper Name    Author
Learning Hidden Markov Models From Pairwise Co-occurrences With Application To Topic Modeling
Kejun Huang;Xiao Fu;Nicholas Sidiropoulos;
Deep Bayesian Nonparametric Tracking
Aonan Zhang;John Paisley;
Learning Registered Point Processes From Idiosyncratic Observations
Hongteng Xu;Lawrence Carin;Hongyuan Zha;


Transfer and Multi-Task Learning


   Paper Name    Author
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman;Eric Tzeng;Taesung Park;Jun-Yan Zhu;Philip Isola;Kate Saenko;Alexei Efros;Prof. Darrell;
Learning Semantic Representations For Unsupervised Domain Adaptation
Shaoan Xie;Zibin Zheng;Liang Chen;Chuan Chen;
Meta-Learning By Adjusting Priors Based On Extended PAC-Bayes Theory
Ron Amit;Ron Meir;
Pseudo-task Augmentation: From Deep Multitask Learning To Intratask Sharingjand Back
Elliot Meyerson;Risto Miikkulainen;
Bilevel Programming For Hyperparameter Optimization And Meta-Learning
Luca Franceschi;Paolo Frasconi;Saverio Salzo;Riccardo Grazzi;Massimiliano Pontil;
Detecting And Correcting For Label Shift With Black Box Predictors
Zachary Lipton;Yu-Xiang Wang;Alexander Smola;
Learning Adversarially Fair And Transferable Representations
David Madras;Elliot Creager;Toniann Pitassi;Richard Zemel;
Rectify Heterogeneous Models With Semantic Mapping
Han-Jia Ye;De-Chuan Zhan;Yuan Jiang;Zhi-Hua Zhou;
Transfer Learning Via Learning To Transfer
Ying WEI;Yu Zhang;Junzhou Huang;Qiang Yang;


Unsupervised Learning


   Paper Name    Author
Deep Density Destructors
David Inouye;Pradeep Ravikumar;
Crowdsourcing With Arbitrary Adversaries
Matth?us Kleindessner;Pranjal Awasthi;
Topological Mixture Estimation
Steve Huntsman;
Revealing Common Statistical Behaviors In Heterogeneous Populations
Andrey Zhitnikov;Rotem Mulayoff;Tomer Michaeli;
Improved Nearest Neighbor Search Using Auxiliary Information And Priority Functions
Omid Keivani;Kaushik Sinha;
Analysis Of Minimax Error Rate For Crowdsourcing And Its Application To Worker Clustering Model
Hideaki Imamura;Issei Sato;Masashi Sugiyama;
Conditional Noise-Contrastive Estimation Of Unnormalised Models
Ciwan Ceylan;Michael Gutmann;
Theoretical Analysis Of Sparse Subspace Clustering With Missing Entries
Manolis Tsakiris;Rene Vidal;
Deep One-Class Classification
Lukas Ruff;Nico G?rnitz;Lucas Deecke;Shoaib Ahmed Siddiqui;Rob Vandermeulen;Alexander Binder;Emmanuel Mȹller;Marius Kloft;
QuantTree: Histograms For Change Detection In Multivariate Data Streams
Giacomo Boracchi;Diego Carrera;Cristiano Cervellera;Danilo MacciȰ;