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neurips_2017_accepted.txt
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neurips_2017_accepted.txt
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Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Zhen He (University College London) · Shaobing Gao (Sichuan University) · Liang Xiao (National University of Defense Technology) · David Barber (University College London)
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
Constantinos Daskalakis (MIT) · Nishanth Dikkala (MIT) · Gautam Kamath (MIT)
Deep Subspace Clustering Network
Pan Ji (University of Adelaide) · Tong Zhang (The Australian National University) · Hongdong Li (Australian National University) · Mathieu Salzmann (EPFL) · Ian Reid (University of Adelaide)
Attentional Pooling for Action Recognition
Rohit Girdhar (Carnegie Mellon University) · Deva Ramanan (Carnegie Mellon University)
On the Consistency of Quick Shift
Heinrich Jiang (Google)
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Yu Liu (The Chinese University of Hong Kong) · Hongyang Li (The Chinese University of Hong Kong) · Xiaogang Wang (The Chinese University of Hong Kong)
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa (UC Berkeley / ETH Zurich) · Rémi Leblond (INRIA) · Simon Lacoste-Julien (INRIA / ENS Paris)
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
Jian Zhao (National University of Singapore) · Lin Xiong (Panasonic R&D; Center Singapore) · Panasonic Karlekar Jayashree (Panasonic, Singapore) · Jianshu Li (National University of Singapore) · Fang Zhao (National University of Singapore) · Zhecan Wang (Franklin. W. Olin College of Engineering) · Panasonic Sugiri Pranata (Panasonic, Singapore) · Panasonic Shengmei Shen (Panasonic, Singapore) · Jiashi Feng (National University of Singapore)
Dilated Recurrent Neural Networks
Shiyu Chang (IBM T.J. Watson Research Center) · Yang Zhang (IBM T. J. Watson Research) · Wei Han (University of Illinois at Urbana-Champaign) · Mo Yu (Johns Hopkins University) · Xiaoxiao Guo (IBM Research) · Wei Tan (IBM T. J. Watson Research Center) · Xiaodong Cui () · Michael Witbrock (IBM Research, USA) · Mark A Hasegawa-Johnson (University of Illinois) · Thomas Huang (UIUC)
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
Saurabh Verma (University of Minnesota) · Zhi-Li Zhang (University of Minnesota)
Scalable Generalized Linear Bandits: Online Computation and Hashing
Kwang-Sung Jun (UW-Madison) · Aniruddha Bhargava (University of Wisconsin-Madison) · Robert Nowak (University of Wisconsion-Madison) · Rebecca Willett (University of Wisconsin)
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris Oates (Newcastle University) · Steven Niederer (Kings College London) · Angela Lee (King's College London) · François-Xavier Briol (University of Warwick) · Mark Girolami (Imperial College London)
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Peva Blanchard () · El Mahdi El Mhamdi (EPFL) · Rachid Guerraoui () · Julien Stainer ()
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi (EPFL) · Rachid Guerraoui () · Hadrien Hendrikx (EPFL) · Alexandre Maurer (EPFL)
Interactive Submodular Bandit
Lin Chen (Yale University) · Andreas Krause (ETHZ) · Amin Karbasi (Yale)
Scene Physics Acquisition via Visual De-animation
Jiajun Wu (MIT) · Erika Lu (University of Oxford) · Pushmeet Kohli (DeepMind) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Zelun Luo (Stanford University) · Yuliang Zou (Virginia Tech) · Judy Hoffman (UC Berkeley) · Li Fei-Fei (Stanford Uniersity)
Decoding with Value Networks for Neural Machine Translation
Di He (Microsoft Research) · Hanqing Lu (Zhejiang University) · Yingce Xia (University of Science and Technology of China) · Tao Qin (Microsoft Research) · Liwei Wang (Peking University) · Tieyan Liu (Microsoft Research)
Parametric Simplex Method for Sparse Learning
Haotian Pang (Princeton University) · Tuo Zhao (Georgia Tech) · Han Liu (Tencent AI Lab) · Robert J Vanderbei (Princeton University)
Group Sparse Additive Machine
Hong Chen (University of Pittsburgh) · Xiaoqian Wang (University of Pittsburgh) · Heng Huang (Computer Science and Engineering University of Texas at Arlington)
Uprooting and Rerooting Higher-order Graphical Models
Adrian Weller (University of Cambridge) · Mark Rowland (University of Cambridge)
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski () · Mark Rowland (University of Cambridge) · Adrian Weller (University of Cambridge)
From Parity to Preference: Learning with Cost-effective Notions of Fairness
Muhammad Bilal Zafar (MPI-SWS) · Isabel Valera () · Manuel Rodriguez (MPI SWS) · Krishna Gummadi (Max Planck Institute for Software Systems) · Adrian Weller (University of Cambridge)
Inferring Generative Model Structure with Static Analysis
Paroma Varma (Stanford University) · Bryan He (Stanford University) · Payal Bajaj (Stanford University) · Nishith Khandwala () · Christopher Ré (Stanford)
Structured Embedding Models for Grouped Data
Maja Rudolph (Columbia University) · Francisco Ruiz () · David Blei (Columbia University)
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum (Gatsby unit, University College London) · Wenkai Xu (Gatsby Unit, UCL) · Zoltan Szabo (Ecole Polytechnique) · Kenji Fukumizu (Institute of Statistical Mathematics) · Arthur Gretton (Gatsby Unit, UCL)
Cortical microcircuits as gated-recurrent neural networks
Rui Costa (University of Oxford) · Ioannis Alexandros Assael (DeepMind) · Brendan Shillingford (University of Oxford) · Nando de Freitas (University of Oxford) · TIm Vogels (University of Oxford)
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
Cong Han Lim (University of Wisconsin-Madison) · Stephen Wright (UW-Madison)
A simple model of recognition and recall memory
Nisheeth Srivastava (IIT Kanpur) · Edward Vul (UCSD)
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin () · Francis Bach (Inria) · Simon Lacoste-Julien (INRIA / ENS Paris)
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Jiasen Lu (Georgia Tech) · Anitha Kannan () · Jianwei Yang (Georgia Tech) · Dhruv Batra () · Devi Parikh (Georgia Tech / Facebook AI Research (FAIR))
MaskRNN: Instance Level Video Object Segmentation
Yuan-Ting Hu (UIUC) · Jia-Bin Huang (Virginia Tech) · Alexander Schwing (University of Illinois at Urbana-Champaign)
Gated Recurrent Convolution Neural Network for OCR
Jianfeng Wang (Beijing University of Posts and Telecommunications) · Xiaolin Hu (Tsinghua University)
Towards Accurate Binary Convolutional Neural Network
Wei Pan (DJI) · Xiaofan Lin (DJI) · Cong Zhao (DJI)
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Wei-Sheng Lai (University of California, Merced) · Jia-Bin Huang (Virginia Tech) · Ming-Hsuan Yang (UC Merced)
Learning a Multi-View Stereo Machine
Abhishek Kar (UC Berkeley) · Jitendra Malik () · Christian Häne (UC Berkeley)
Phase Transitions in the Pooled Data Problem
Jonathan Scarlett (EPFL) · Volkan Cevher (EPFL)
Universal Style Transfer via Feature Transforms
Yijun Li (University of California, Merced) · Chen Fang (Adobe Research) · Jimei Yang (Adobe Research) · Zhaowen Wang (Adobe Research) · Xin Lu (Adobe) · Ming-Hsuan Yang (UC Merced)
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Iku Ohama (Panasonic Corporation) · Issei Sato (The University of Tokyo/RIKEN) · Takuya Kida (Hokkaido University) · Hiroki Arimura (Hokkaido University)
Pose Guided Person Image Generation
Liqian Ma (KU Leuven) · Xu Jia (KU Leuven) · Qianru Sun (MPI Informatics) · Bernt Schiele (Max Planck Institute for Informatics) · Tinne Tuytelaars (KU Leuven) · Luc Van Gool (KU Leuven)
Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat Erdogdu (Microsoft Research) · Yash Deshpande (MIT) · Andrea Montanari (Stanford)
Variable Importance Using Decision Trees
Arash A Amini (UCLA) · Seyed Jalil Kazemitabar (University of California, Los Angeles) · Adam Bloniarz (Google) · Ameet S Talwalkar (CMU)
Preventing Gradient Explosions in Gated Recurrent Units
Sekitoshi Kanai (NTT) · Yasuhiro Fujiwara (NTT Software Innovation Center) · Sotetsu Iwamura (NTT Software Innovation center)
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon Du (Carnegie Mellon University) · Yining Wang (Carnegie Mellon University) · Aarti Singh (CMU)
f-GANs in an Information Geometric Nutshell
Richard Nock (Data61, The Australian National University & The University of Sydney) · Zac Cranko (The Australian National University & Data61) · Aditya K Menon (Data61/CSIRO) · Lizhen Qu (Data61) · Robert C Williamson (Australian National University & Data61)
Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency
Jun-Yan Zhu (UC Berkeley) · Richard Zhang (University of California, Berkeley) · Deepak Pathak (UC Berkeley) · Trevor Darrell (UC Berkeley) · Oliver Wang (Adobe Research) · Eli Shechtman () · Alexei Efros (UC Berkeley)
Mixture-Rank Matrix Approximation for Collaborative Filtering
Dongsheng Li (IBM Research - China) · Chao Chen (Tongji University) · Wei Liu (Tencent Technology (Shenzhen) Company Limited) · Tun Lu (Fudan University) · Ning Gu (Fudan University) · Stephen Chu (IBM Research - China)
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
An Bian (ETH Zurich) · Joachim M Buhmann (ETH Zurich) · Andreas Krause (ETHZ) · Kfir Levy (ETH)
Learning with Average Top-k Loss
Yanbo Fan (NLPR, CASIA) · Siwei Lyu (SUNY at Albany) · Yiming Ying (State University of New York at Albany) · Baogang Hu (Chinese Academy of Sciences)
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi (University of Oxford) · Hakan Bilen (University of Oxford) · Andrea Vedaldi (University of Oxford)
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
Ryan Tibshirani (Carnegie Mellon University)
Flat2Sphere: Learning Spherical Convolution for Fast Features from 360° Imagery
Yu-Chuan Su (UT Austin) · Kristen Grauman (UT Austin)
3D Shape Reconstruction by Modeling 2.5D Sketch
Jiajun Wu (MIT) · Yifan Wang (ShanghaiTech University) · Tianfan Xue (MIT CSAIL) · Xingyuan Sun (Shanghai Jiao Tong University) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)
Multimodal Learning and Reasoning for Visual Question Answering
Ilija Ilievski (National University of Singapore) · Jiashi Feng (National University of Singapore)
Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony (University of Illinois at Chicago) · Mohammad Ali Bashiri (University of Illinois at Chicago) · Brian Ziebart (University of Illinois at Chicago)
Hypothesis Transfer Learning via Transformation Functions
Simon Du (Carnegie Mellon University) · Jayanth Koushik (Carnegie Mellon University) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)
Adversarial Invariant Feature Learning
Qizhe Xie (Carnegie Mellon University) · Zihang Dai (CMU) · Yulun Du (Carnegie Mellon University) · Eduard Hovy (CMU) · Graham Neubig (Carnegie Mellon University)
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Yuanzhi Li (Princeton University) · Yang Yuan (Cornell University)
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Tomoya Murata (NTT DATA Mathematical Systems Inc.) · Taiji Suzuki ()
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Nanyang Ye (University of Cambridge) · Zhanxing Zhu (Peking University & Beijing Institute of Big Data Research) · Rafal Mantiuk (University of Cambridge)
Efficient Online Linear Optimization with Approximation Algorithms
Dan Garber (Technion - Israel Institute of Technology)
Geometric Descent Method for Convex Composite Minimization
Shixiang Chen (The Chinese University of HongKong) · Shiqian Ma (UC Davis) · Wei Liu (Tencent Technology (Shenzhen) Company Limited)
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Chris Junchi Li (Princeton University) · Mengdi Wang (Princeton University) · Tong Zhang (Tencent AI Lab)
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus (MPI Tuebingen & Cambridge) · Mateo Rojas Carulla (University of Cambridge, Max Planck for Intelligent Systems) · Giambattista Parascandolo () · Moritz Hardt (UC Berkeley) · Dominik Janzing (MPI Tübingen) · Bernhard Schölkopf (MPI for Intelligent Systems)
Nonparametric Online Regression while Learning the Metric
Ilja Kuzborskij (EPFL) · Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy)
Recycling for Fairness: Learning with Conditional Distribution Matching Constraints
Novi Quadrianto (University of Sussex and HSE) · Viktoriia Sharmanska (Imperial College London)
Safe and Nested Subgame Solving for Imperfect-Information Games
Noam Brown (Carnegie Mellon University) · Tuomas Sandholm (Carnegie Mellon University)
Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu (NVIDIA) · Thomas Breuel () · Jan Kautz (NVIDIA)
Coded Distributed Computing for Inverse Problems
Yaoqing Yang (Carnegie Mellon University) · Pulkit Grover (CMU) · Soummya Kar (Carnegie Mellon University)
A Screening Rule for l1-Regularized Ising Model Estimation
Zhaobin Kuang (University of Wisconsin, Madison) · Sinong Geng (University of Wisconsin Madison) · David Page (UW-Madison)
Improved Dynamic Regret for Non-degeneracy Functions
Lijun Zhang (Nanjing University (NJU)) · Tianbao Yang (The University of Iowa) · Jinfeng Yi (IBM Thomas J. Watson Research Center) · Rong Jin () · Zhi-Hua Zhou (Nanjing University)
Learning Efficient Object Detection Models with Knowledge Distillation
Guobin Chen (University of Missouri) · Wongun Choi (NEC Laboratories) · Xiang Yu (NEC Laboratories America) · Tony Han (University of Missouri) · Manmohan Chandraker (University of California, San Diego)
One-Sided Unsupervised Domain Mapping
Sagie Benaim (Tel Aviv University) · Lior Wolf (Facebook AI Research)
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli (Universität Bern) · Matthias Zwicker (University of Maryland, College Park) · Paolo Favaro (University of Bern) · Meiguang Jin (University of Bern)
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello (MPI - ETH Zürich) · Michael Tschannen (ETH Zurich) · Gunnar Raetsch (ETHZ) · Martin Jaggi (EPFL)
A New Theory for Nonconvex Matrix Completion
Guangcan Liu (Nanjing University of Information Science & Technology (NUIST)) · Xiaotong Yuan () · Qingshan Liu ()
Robust Hypothesis Test for Functional Effect with Gaussian Processes
Jeremiah Liu (Harvard University) · Brent Coull (Harvard University)
Lower bounds on the robustness to adversarial perturbations
Jonathan Peck (Ghent University) · Yvan Saeys (Ghent University) · Bart Goossens (Ghent University) · Joris Roels (Ghent University)
Minimizing a Submodular Function from Samples
Eric Balkanski (Harvard University) · Yaron Singer (Harvard University)
Introspective Classification with Convolutional Nets
Long Jin (University of California San Diego) · Justin Lazarow (UC San Diego) · Zhuowen Tu ()
Label Distribution Learning Forests
Wei Shen (Shanghai University) · KAI ZHAO (Nankai University) · Yilu Guo (Shanghai University) · Alan Yuille (Johns Hopkins University)
Unsupervised object learning from dense equivariant image labelling
James Thewlis (University of Oxford) · Andrea Vedaldi (University of Oxford) · Hakan Bilen (University of Oxford)
Compression-aware Training of Deep Neural Networks
Jose Alvarez (TRI) · Mathieu Salzmann (EPFL)
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
Daniel Milstein (Brown University) · Jason Pacheco (Brown University) · Brown Leigh Hochberg (Brown, MGH, VA, Harvard) · John D Simeral (Brown University) · Beata Jarosiewicz (Stanford University) · Erik Sudderth (University of California, Irvine)
PredRNN: Recurrent Neural Networks for Video Prediction using Spatiotemporal LSTMs
Yunbo Wang (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Philip S Yu (UIC)
Detrended Partial Cross Correlation for Brain Connectivity Analysis
Jaime Ide (Yale University) · Fábio Cappabianco (Federal University of Sao Paulo) · Fabio Faria (Federal University of Sao Paulo) · Chiang-shan R Li (Yale University)
Contrastive Learning for Image Captioning
Bo Dai (The Chinese University of Hong Kong) · Dahua Lin (The Chinese University of Hong Kong)
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp (ETH Zurich) · Matteo Turchetta (ETH Zurich) · Angela Schoellig () · Andreas Krause (ETHZ)
Online multiclass boosting
Young Hun Jung (Universith of Michigan) · Jack Goetz (University of Michigan) · Ambuj Tewari (University of Michigan)
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Sheng Li (Adobe Research) · Yun Fu (Northeastern University)
Learning Overcomplete HMMs
Vatsal Sharan (Stanford University) · Sham Kakade (University of Washington) · Percy Liang (Stanford University) · Gregory Valiant (Stanford University)
GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni (Donders Institute) · Max Hinne (Radboud University) · Marcel Van Gerven (Radboud University) · Eric Maris (Donders Institute)
Decoupling "when to update" from "how to update"
Eran Malach (Hebrew University Jerusalem Israel) · Shai Shalev-Shwartz (Mobileye & HUJI)
Self-Normalizing Neural Networks
Günter Klambauer (LIT AI Lab / University Linz) · Thomas Unterthiner (LIT AI Lab / University Linz) · Andreas Mayr (LIT AI Lab / University Linz) · Sepp Hochreiter (LIT AI Lab / University Linz)
Learning to Pivot with Adversarial Networks
Gilles Louppe (New York University) · Michael Kagan (SLAC / Stanford) · Kyle Cranmer (New York University)
MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt (TU Berlin) · Pieter-Jan Kindermans (Google Brain Resident) · Huziel E. Sauceda (Fritz-Haber-Institut der Max-Planck-Gesellschaft) · Stefan Chmiela (Technische Universität Berlin) · Alexandre Tkatchenko (University of Luxembourg) · Klaus-Robert Müller (TU Berlin)
Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples
Haw-Shiuan Chang (UMass, Amherst) · Andrew McCallum (UMass Amherst) · Erik Learned-Miller (UMass Amherst)
Differentiable Learning of Submodular Functions
Josip Djolonga (ETH Zurich) · Andreas Krause (ETHZ)
Inductive Representation Learning on Large Graphs
Will Hamilton (Stanford University) · Zhitao Ying (Stanford University) · Jure Leskovec (Stanford University)
Subset Selection for Sequential Data
Ehsan Elhamifar (Northeastern University)
Question Asking as Program Generation
Anselm Rothe (New York University) · Brenden Lake (New York University) · Todd Gureckis (New York University)
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Songbai Yan (University of California, San Diego) · Chicheng Zhang (University of California San Diego)
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon Du (Carnegie Mellon University) · Chi Jin (UC Berkeley) · Jason D Lee (USC) · Michael Jordan (UC Berkeley) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer Bouchard (Lawrence Berkeley National Laboratory) · Alejandro Bujan (UC Berkeley) · Farbod Roosta-Khorasani (University of California Berkeley) · Shashanka Ubaru (University of Minnesota) · Mr. Prabhat (LBL/NERSC) · Antoine Snijders () · Jian-Hua Mao () · Edward Chang () · Michael W Mahoney (UC Berkeley) · Sharmodeep Bhattacharya ()
One-Shot Imitation Learning
Yan Duan (UC Berkeley) · Marcin Andrychowicz (OpenAI) · Bradly Stadie (OpenAI) · OpenAI Jonathan Ho (OpenAI, UC Berkeley) · Jonas Schneider (OpenAI) · Ilya Sutskever () · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Wojciech Zaremba (OpenAI)
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Mainak Jas (Télécom ParisTech) · Tom Dupré la Tour (Télécom ParisTech) · Umut Simsekli (Bogazici University) · Alexandre Gramfort (LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay)
Integration Methods and Optimization Algorithms
Damien Scieur (INRIA - ENS) · Vincent Roulet (INRIA / ENS Ulm) · Francis Bach (Inria) · Alexandre d'Aspremont (CNRS - Ecole Normale Supérieure)
Sharpness, Restart and Acceleration
Vincent Roulet (INRIA / ENS Ulm) · Alexandre d'Aspremont (CNRS - Ecole Normale Supérieure)
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi (The University of Tokyo) · Yoshinobu Kawahara (Osaka University) · Takehisa Yairi (The University of Tokyo)
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson (ETH Zurich) · Fabian Mentzer (ETH Zurich) · Michael Tschannen (ETH Zurich) · Lukas Cavigelli (ETH Zurich) · Radu Timofte (ETH Zurich) · Luca Benini (ETH Zurich) · Luc V Gool (Computer Vision Lab, ETH Zurich)
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Stéphanie ALLASSONNIERE (Ecole Polytechnique) · Juliette Chevallier (École polytechnique)
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Qinshi Wang (Princeton University) · Wei Chen (Microsoft Research)
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman (Carnegie Mellon University) · Nicholas Rhinehart (Carnegie Mellon University) · Wen Sun (Carnegie Mellon University) · Lerrel Pinto () · Martial Hebert (cmu) · Byron Boots (Georgia Tech / Google Brain) · Kris Kitani (Carnegie Mellon University) · J. Bagnell (Carnegie Mellon University)
Posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal () · Randy Jia (Columbia University)
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen (The Curious AI Company) · Harri Valpola (The Curious AI Company)
Matching neural paths: transfer from recognition to correspondence search
Nikolay Savinov (ETH Zurich) · Lubor Ladicky (ETH Zurich) · Marc Pollefeys (ETH Zurich)
Linearly constrained Gaussian processes
Carl Jidling (Uppsala University) · Niklas Wahlström (Uppsala University) · Adrian Wills (University of Newcastle, Australia) · Thomas B Schön (Uppsala University)
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Joel A Tropp (Caltech) · Alp Yurtsever (École Polytechnique Fédérale de Lausanne, Switzerland) · Madeleine Udell (Cornell) · Volkan Cevher (EPFL)
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Karol Hausman (University of Southern California) · Yevgen Chebotar (University of Southern California) · Stefan Schaal (USC) · Gaurav Sukhatme (USC) · Joseph J Lim (University of Southern California)
Learning to Inpaint for Image Compression
Mohammad Haris Baig (Dartmouth College) · Vladlen Koltun (Intel Labs) · Lorenzo Torresani (Dartmouth)
Adaptive Bayesian Sampling with Monte Carlo EM
Anirban Roychowdhury (Ohio State University) · Srinivasan Parthasarathy (The Ohio State University)
No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization
Yi Xu (The University of Iowa) · Mingrui Liu (The University of Iowa) · Tianbao Yang (The University of Iowa) · Univ Qihang Lin (Univ Iowa, faculty)
Shape and Material from Sound
zhoutong zhang (MIT) · Qiujia Li (University of Cambridge) · Zhengjia Huang () · Jiajun Wu (MIT) · Josh Tenenbaum (MIT) · Bill Freeman (MIT/Google)
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann (research center caesar, an associate of the Max Planck Society) · Pedro J Goncalves (research center caesar, an associate of the Max Planck Society) · Giacomo Bassetto (research center caesar) · Kaan Oecal (research center caesar) · Marcel Nonnenmacher (research center caesar, an associate of the Max Planck Society) · Jakob H Macke (research center caesar, an associate of the Max Planck Society)
Online Prediction with Selfish Experts
Tim Roughgarden (Stanford University) · Okke Schrijvers (Facebook Inc.)
Tensor Biclustering
Soheil Feizi (Stanford University) · Hamid Javadi (Stanford University) · David Tse (Stanford University)
DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja (University of California, Los Angeles) · William Zame (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang (University of California, Berkeley) · Mukul Gagrani (University of Southern California) · Ashutosh Nayyar (University of Southern California) · Rahul Jain (University of Southern California)
Testing and Learning on Distributions with Symmetric Noise Invariance
Law Ho Chung (University Of Oxford) · Christopher Yau (University of Oxford) · Dino Sejdinovic (University of Oxford)
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Hongteng Xu (Duke University) · Hongyuan Zha (Georgia Tech)
Deanonymization in the Bitcoin P2P Network
Giulia Fanti (Carnegie Mellon University) · Pramod Viswanath (UIUC)
Accelerated consensus via Min-Sum Splitting
Patrick Rebeschini (University of Oxford) · Sekhar C Tatikonda (Yale University)
Generalized Linear Model Regression under Distance-to-set Penalties
Jason Xu (NSF Postdoctoral Fellow UCLA) · Eric Chi (North Carolina State University) · Kenneth Lange (UCLA)
Adaptive sampling for a population of neurons
Benjamin Cowley (Carnegie Mellon University) · Ryan Williamson (Carnegie Mellon University) · Katerina Clemens (University of Pittsburgh) · Matthew Smith (University of Pittsburgh) · Byron M Yu (Carnegie Mellon University)
Nonbacktracking Bounds on the Influence in Independent Cascade Models
Emmanuel Abbe (Princeton University) · Sanjeev Kulkarni (Princeton University) · Eun Jee Lee (Princeton University)
Learning with Feature Evolvable Streams
Bo-Jian Hou (LAMDA Group) · Lijun Zhang (Nanjing University (NJU)) · Zhi-Hua Zhou (Nanjing University)
Online Convex Optimization with Stochastic Constraints
Hao Yu (University of Southern California) · Michael Neely (Univ. Southern California) · Xiaohan Wei (University of Southern California)
Max-Margin Invariant Features from Transformed Unlabelled Data
Dipan Pal (Carnegie Mellon University) · Ashwin Kannan (Carnegie Mellon University) · Gautam Arakalgud (Carnegie Mellon University) · Marios Savvides (Carnegie Mellon University)
Cognitive Impairment Prediction in Alzheimer’s Disease with Regularized Modal Regression
Xiaoqian Wang (University of Pittsburgh) · Hong Chen (University of Pittsburgh) · Dinggang Shen (UNC-Chapel Hill) · Heng Huang (Computer Science and Engineering University of Texas at Arlington)
Translation Synchronization via Truncated Least Squares
Xiangru Huang (UT Austin) · Zhenxiao Liang (Tsinghua University) · Chandrajit Bajaj (The University of Texas at Austin) · Qixing Huang (The University of Texas at Austin)
From which world is your graph
Cheng Li (College of William and Mary) · Varun Kanade (University of Oxford) · Felix MF Wong (Google) · Zhenming Liu (William and Mary)
A New Alternating Direction Method for Linear Programming
Sinong Wang (The Ohio State University) · Ness Shroff (The Ohio State University)
Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai (Kanagawa University)
Best Response Regression
Omer Ben Porat (Technion – Israel Institute of Technology) · Moshe Tennenholtz (Technion--Israel Institute of Technology)
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen (Duke University) · Cong Xu (Hewlett Packard Labs) · Feng Yan (University of Nevada, Reno) · Chunpeng Wu (Duke University) · Yandan Wang (University of Pittsburgh) · Yiran Chen (Duke University) · Hai Li (Duke University)
Learning Affinity via Spatial Propagation Networks
Sifei Liu (Nvidia) · Guangyu Zhong (Dalian University of Technology) · Ming-Hsuan Yang (UC Merced) · Shalini De Mello (NVIDIA) · Jan Kautz (NVIDIA) · Jinwei Gu (NVIDIA Research)
Linear regression without correspondence
Daniel Hsu () · Kevin Shi (Columbia University) · Xiaorui Sun (Columbia University)
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Martin J Zhang (Stanford University) · Fei Xia (Stanford University) · James Zou (Stanford) · David Tse (Stanford University)
Cost efficient gradient boosting
Sven Peter (University Heidelberg) · Ferran Diego () · Fred Hamprecht (Heidelberg University) · Boaz Nadler (Weizmann Institute of Science)
Probabilistic Rule Realization and Selection
Haizi Yu (University of Illinois at Urbana-Champaign) · Tianxi Li (University of Michigan) · Lav Varshney (University of Illinois at Urbana-Champaign)
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich (Ben Gurion University) · Sivan Sabato (Ben Gurion University) · Roi Weiss (Weizmann institute of science)
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Tor Lattimore (DeepMind)
Learning Multiple Tasks with Deep Relationship Networks
Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Philip S Yu (UIC)
Deep Hyperalignment
Muhammad Yousefnezhad (Nanjing University of Aeronautics and Astronautics) · Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics)
Online to Offline Conversions and Adaptive Minibatch Sizes
Kfir Levy (ETH)
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Alberto Bietti (Inria) · Julien Mairal (Inria)
Deep Learning with Topological Signatures
Christoph Hofer (University of Salzburg) · Roland Kwitt (University of Salzburg) · Marc Niethammer (UNC Chapel Hill) · Andreas Uhl (University of Salzburg)
Predicting User Activity Level In Point Process Models With Mass Transport Equation
Yichen Wang (Georgia Tech) · Xiaojing Ye (Georgia State University) · Hongyuan Zha (Georgia Tech) · Le Song (Georgia Institute of Technology)
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Noga Alon (Tel Aviv University) · Moshe Babaioff (Microsoft Research) · Yannai A. Gonczarowski (The Hebrew University of Jerusalem and Microsoft Research) · Yishay Mansour (Tel Aviv University) · Shay Moran (IAS, Princeton) · Amir Yehudayoff (Technion - Israel institue of Technology)
Deep Dynamic Poisson Factorization Model
Chengyue Gong (PeKing University) · win-bin huang (peking university)
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo (UTokyo/RIKEN) · Gang Niu (The University of Tokyo) · Marthinus C du Plessis (The University of Tokyo) · Masashi Sugiyama (RIKEN / University of Tokyo)
Optimal Sample Complexity of M-wise Data for Top-K Ranking
Minje Jang (KAIST) · Sunghyun Kim (ETRI) · Changho Suh (KAIST) · Sewoong Oh (UIUC)
What-If Reasoning using Counterfactual Gaussian Processes
Peter Schulam (Johns Hopkins University) · Suchi Saria (Johns Hopkins University)
Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks
Dan Alistarh (IST Austria & ETH Zurich) · Demjan Grubic (ETH Zurich) · Jerry Li (MIT) · Ryota Tomioka (Microsoft Research Cambridge) · Milan Vojnovic (London School of Economics and Political Science (LSE))
On the Convergence of Block Coordinate Descent in Training DNNs with Tikhonov Regularization
Ziming Zhang (MERL) · Matthew Brand (Mitsubishi Electric Research Labs)
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer (Technion) · Itay Hubara (Technion) · Daniel Soudry (Technion)
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Arjun K Bansal (Intel Nervana) · William Constable (Intel) · Oguz Elibol (Intel) · Stewart Hall (Intel) · Luke Hornof (Intel) · Amir Khosrowshahi (Intel) · Carey Kloss (Intel) · Urs Köster (Intel Corporation) · Marcel Nassar (Intel Corporation) · Naveen Rao (Intel) · Xin Wang (Intel Corporation) · Tristan Webb (Intel / Nervana)
Model evidence from nonequilibrium simulations
Michael Habeck (Max Planck Institute Goettingen)
Minimal Exploration in Structured Stochastic Bandits
Stefan Magureanu (KTH) · Richard Combes (Centrale-Supelec) · Alexandre Proutiere (KTH)
Learned D-AMP: Principled Neural-network-based Compressive Image Recovery
Chris Metzler (Rice University) · Ali Mousavi (Rice University) · Richard Baraniuk (Rice University)
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
Yingce Xia (University of Science and Technology of China) · Lijun Wu (Sun Yat-sen University) · Jianxin Lin (USTC) · Fei Tian (Miicrosoft Research) · Tao Qin (Microsoft Research) · Tie-Yan Liu (Microsoft Research)
Adaptive Clustering through Semidefinite Programming
Martin Royer (Université Paris-Saclay)
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Liangpeng Zhang (University of Birmingham) · Ke Tang (University of Science and Technology of China) · Xin Yao (University of Birmingham)
Repeated Inverse Reinforcement Learning
Kareem Amin (Google Research) · Nan Jiang (Microsoft Research) · Satinder Singh (University of Michigan)
The Numerics of GANs
Lars Mescheder (Max-Planck Institute Tuebingen) · Sebastian Nowozin (Microsoft Research Cambridge) · Andreas Geiger (MPI Tübingen)
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Luigi Acerbi (New York University) · Wei Ji (New York University)
Learning Chordal Markov Networks via Branch and Bound
Kari Rantanen (University of Helsinki) · Antti Hyttinen (University of Helsinki) · Matti Järvisalo (University of Helsinki)
Revenue Optimization with Approximate Bid Predictions
Andres Munoz () · Sergei Vassilvitskii (Google)
Solving (Almost) all Systems of Random Quadratic Equations
Gang Wang (University of Minnesota) · Georgios Giannakis (University of Minnesota) · Yousef Saad (University of Minnesota) · Jie Chen (Beijing Institute of Technology)
Unsupervised Learning of Disentangled Latent Representations from Sequential Data
Wei-Ning Hsu (Massachusetts Institute of Technology) · Yu Zhang (Google Brain) · James Glass (MIT CSAIL)
Lookahead Bayesian Optimization with Inequality Constraints
Remi Lam (MIT) · Karen Willcox (MIT)
Hierarchical Methods of Moments
Matteo Ruffini (UPC) · Guillaume Rabusseau (McGill University) · Borja Balle ()
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Raymond Yeh (University of Illinois at Urbana–Champaign) · Jinjun Xiong (IBM Research) · Wen-Mei Hwu () · Minh Do (University of Illinois) · Alexander Schwing (University of Illinois at Urbana-Champaign)
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Lixin Fan (Nokia Technologies)
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Pan Xu (University of Virginia) · Jian Ma (Carnegie Mellon University) · Quanquan Gu (University of Virginia)
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe (Google)
Generating steganographic images via adversarial training
Jamie Hayes (University College London) · George Danezis (University College London)
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Jason Altschuler (MIT) · Jonathan Weed (MIT) · Philippe Rigollet (MIT)
PixelGAN Autoencoders
Alireza Makhzani (University of Toronto) · Brendan J Frey (Deep Genomics, Vector Institute, Univ. Toronto)
Consistent Multitask Learning with Nonlinear Output Relations
Carlo Ciliberto (University College London) · Alessandro Rudi (University of Genova) · Lorenzo Rosasco (University of Genova- MIT - IIT) · Massimiliano Pontil (University College London & Italian Institute of Technology)
Fast Alternating Minimization Algorithms for Dictionary Learning
Niladri Chatterji (UC Berkeley) · Peter Bartlett (UC Berkeley)
Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi (University of Southern california)
Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth (ETH) · Aurelien Lucchi (ETH Zurich) · Sebastian Nowozin (Microsoft Research Cambridge) · Thomas Hofmann (ETH Zurich)
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Wen Dong (University at Buffalo) · Le Fang (University at Buffalo-SUNY) · Fan Yang (University at Buffalo) · Tong Guan () · Chunming Qiao ()
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Rowan McAllister (University of Cambridge) · Carl Edward Rasmussen (University of Cambridge)
Compatible Reward Inverse Reinforcement Learning
Alberto Maria Metelli (Politecnico di Milano) · Matteo Pirotta (INRIA Lille-Nord Europe) · Marcello Restelli ()
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)
Hiding Images in Plain Sight: Deep Steganography
Google Shumeet Baluja (Google, Inc.)
Neural Program Meta-Induction
Jacob Devlin (Microsoft Research) · Rudy R Bunel (Oxford University) · Rishabh Singh (Microsoft Research) · Matthew Hausknecht (Microsoft Research) · Pushmeet Kohli (DeepMind)
Bayesian Dyadic Trees and Histograms for Regression
Stéphanie van der Pas (Leiden University) · Veronika Rockova (University of Chicago)
A graph-theoretic approach to multitasking
Noga Alon (Tel Aviv University) · Daniel Reichman (University of California, Berkeley) · Igor Shinkar (UC Berkeley) · Tal Wagner (MIT) · Sebastian Musslick () · Tom Griffiths (UC Berkeley) · Jonathan D Cohen (Princeton University) · Biswadip dey (Princeton University) · Kayhan Ozcimder (Princeton University)
Consistent Robust Regression
Kush Bhatia (UC Berkeley) · Prateek Jain (Microsoft Research) · Purushottam Kar (Indian Institute of Technology Kanpur)
Natural value approximators: learning when to trust past estimates
Tom Schaul (DeepMind) · Zhongwen Xu (DeepMind) · Joseph Modayil (Deepmind) · Hado van Hasselt (DeepMind) · Andre Barreto (DeepMind) · David Silver (DeepMind)
Bandits Dueling on Partially Ordered Sets
CMLA Julien Audiffren (CMLA, ENS CACHAN) · Liva Ralaivola (LIF, Aix-Marseille University, CNRS)
Elementary Symmetric Polynomials for Optimal Experimental Design
Zelda E. Mariet (MIT) · Suvrit Sra (MIT)
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov (University of Amsterdam) · Ivan Titov ()
Backprop without Learning Rates Through Coin Betting
Francesco Orabona (Stony Brook University) · Tatiana Tommasi (University of Rome La Sapienza)
Pixels to Graphs by Associative Embedding
Alejandro Newell (University of Michigan) · Jia Deng (University of Michigan)
Runtime Neural Pruning
Ji Lin (Tsinghua University) · Yongming Rao (Tsinghua University) · Jiwen Lu (Tsinghua University)
Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time
Surbhi Goel (University of Texas at Austin) · Adam Klivans (UT Austin)
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li (Carnegie Mellon University) · Wei-Cheng Chang (Carnegie Mellon University) · Yu Cheng (AI Foundations, IBM Research) · Yiming Yang (CMU) · Barnabas Poczos (Carnegie Mellon University)
The Reversible Residual Network: Backpropagation Without Storing Activations
Aidan N Gomez (University of Toronto) · Mengye Ren (University of Toronto) · Raquel Urtasun (University of Toronto) · Roger Grosse (University of Toronto)
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Quentin Berthet (University of Cambridge) · Vianney Perchet (ENS Paris-Saclay & Criteo Research)
Zap Q-Learning
Adithya M Devraj (University of Florida) · Sean P Meyn (University of Florida)
Expectation Propagation for t-Exponential Family Using Q-Algebra
Futoshi Futami (University of Tokyo/RIKEN) · Issei Sato (The University of Tokyo/RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou (University of Toronto) · Richard Zemel (University of Toronto) · Raquel Urtasun (University of Toronto)
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Matthias Hein (Saarland University) · Maksym Andriushchenko (Saarland University)
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Alejandro Newell (University of Michigan) · Zhiao Huang (IIIS, Tsinghua University) · Jia Deng (University of Michigan)
Practical Locally Private Heavy Hitters
kobbi nissim (Georgetown University) · Raef Bassily (UCSD) · Uri Stemmer (Harvard University) · Abhradeep Thakurta (APPLE Inc)
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Kinjal Basu (LinkedIn) · Ankan Saha (University of Chicago) · Shaunak Chatterjee ()
Inhomogoenous Hypergraph Clustering with Applications
Pan Li (University of Illinois Urbana-Champaign) · Olgica Milenkovic (University of Illinois at Urbana-Champaign)
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Fan Yang (Carnegie Mellon University) · Zhilin Yang (Carnegie Mellon University) · William W Cohen (Carnegie Mellon University)
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Ahmed Ibrahim (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)
Masked Autoregressive Flow for Density Estimation
George Papamakarios (University of Edinburgh) · Iain Murray (University of Edinburgh) · Theo Pavlakou (The University of Edinburgh)
Non-convex Finite-Sum Optimization Via SCSG Methods
Lihua Lei (UC Berkeley) · Cheng Ju (University of California, Berkeley) · Jianbo Chen (University of California, Berkeley) · Michael Jordan (UC Berkeley)
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Rebecca Morrison (Massachusetts Institute of Technology) · Ricardo Baptista (MIT) · Youssef Marzouk (Massachusetts Institute of Technology)
Inner-loop free ADMM using Auxiliary Deep Neural Networks
Kai Fan (Duke University) · Qi Wei (Duke University) · Katherine A Heller (Duke)
OnACID: Online Analysis of Calcium Imaging Data in Real Time
Andrea Giovannucci (Flatiron Institute, Simons Foundation) · Johannes Friedrich (Columbia University) · Matt Kaufman (Cold Spring Harbor Laboratory) · Anne Churchland (Cold Spring Harbor Laboratory) · Dmitri Chklovskii (Simons Foundation) · Liam Paninski (Columbia University) · Eftychios Pnevmatikakis (Flatiron Institute)
Collaborative PAC Learning
Avrim Blum (CMU) · Nika Haghtalab (Carnegie Mellon University) · Ariel D Procaccia (Carnegie Mellon University) · IIIS Mingda Qiao (IIIS, Tsinghua University)
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier (UC Berkeley) · Michael Jordan (UC Berkeley) · Jon McAuliffe (UC Berkeley)
Scalable Demand-Aware Recommendation
Jinfeng Yi (IBM Thomas J. Watson Research Center) · Cho-Jui Hsieh (UC Davis) · Kush R Varshney (IBM Research) · Lijun Zhang (Nanjing University (NJU)) · Yao Li (University of California, Davis)
SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely (Google Research)
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Yichong Xu (Carnegie Mellon University) · Hongyang Zhang (Carnegie Mellon University) · Aarti Singh (Carnegie Mellon University) · Artur Dubrawski (Carnegie Mellon University) · Kyle Miller (Carnegie Mellon University)
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Yonatan Belinkov (MIT)
Generative Local Metric Learning for Kernel Regression
Yung-Kyun Noh (Seoul National University) · Masashi Sugiyama (RIKEN / University of Tokyo) · Kee-Eung Kim (KAIST) · Frank Park (Seoul National University) · Daniel Lee (University of Pennsylvania)
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Linus Hamilton (MIT) · Frederic Koehler (MIT) · Ankur Moitra ()
Fitting Low-Rank Tensors in Constant Time
Kohei Hayashi (AIST / RIKEN) · Yuichi Yoshida (National Institute of Informatics and Preferred Infrastructure, Inc.)
Deep supervised discrete hashing
Qi Li (Institute of Automation, Chinese Academy of Sciences) · Zhenan Sun (Institute of Automation, Chinese Academy of Sciences (CASIA)) · Ran He (CASIA) · Tieniu Tan (Chinese Academy of Sciences)
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Zhaohan Guo (Carnegie Mellon University/Stanford) · Philip S. Thomas (CMU) · Emma Brunskill (CMU)
How regularization affects the critical points in linear networks
Amirhossein Taghvaei (University of Illinois at Urbana-Champaign) · Jin W Kim (University of Illinois) · Prashant Mehta (University of Illinois)
Fisher GAN
Youssef Mroueh (IBM T.J Watson Research Center) · Tom Sercu (IBM Research)
Information-theoretic analysis of generalization capability of learning algorithms
Maxim Raginsky (University of Illinois at Urbana-Champaign) · Aolin Xu (University of Illinois at Urbana-Champaign)
Sparse Approximate Conic Hulls
Greg Van Buskirk (UT Dallas) · Ben Raichel (UT Dallas) · Nicholas Ruozzi (UTDallas)
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Alyson Fletcher (UCLA, UCSC, & UC Berkeley) · Sundeep Rangan (NYU-Poly) · Mojtaba Sahraee-Ardakan (UCLA) · Philip Schniter (Ohio State University)
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Chengxu Zhuang (Stanford University) · Jonas Kubilius (Massachusetts Institute of Technology) · Mitra JZ Hartmann (Northwestern University) · Daniel Yamins (Stanford University)
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Steven Wu (Microsoft Research) · Bo Waggoner () · Seth Neel (University of Pennsylvania) · Aaron Roth (University of Pennsylvania) · Katrina Ligett ()
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu (UC Berkeley) · John Co-Reyes (UC Berkeley) · Sergey Levine (UC Berkeley)
Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau (McGill University) · Borja Balle () · Joelle Pineau (McGill University)
Multi-way Interacting Regression via Factorization Machines
Mikhail Yurochkin (University of Michigan) · XuanLong Nguyen (University of Michigan) · nikolaos Vasiloglou (LogicBlox)
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin (MIT CSAIL) · Connor W Coley (MIT Department of Chemical Engineering) · Regina Barzilay (Massachusetts Institute of Technology) · Tommi Jaakkola (MIT)
Practical Data-Dependent Metric Compression with Provable Guarantees
Piotr Indyk (MIT) · Ilya Razenshteyn (Columbia University) · Tal Wagner (MIT)
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker (Google Brain) · Andriy Mnih () · Chris J Maddison (University of Oxford / DeepMind) · John Lawson (Google Brain) · Jascha Sohl-Dickstein (Google Brain)
Nonlinear random matrix theory for deep learning
Jeffrey Pennington (Google Brain) · Pratik Worah (Google)
Parallel Streaming Wasserstein Barycenters
Matthew Staib (MIT) · Sebastian Claici (MIT) · Justin M Solomon (MIT) · Stefanie Jegelka (MIT)
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Yuandong Tian (Facebook AI Research) · Qucheng Gong (Facebook AI Research) · Wenling Shang (University of Amsterdam) · Yuxin Wu (Facebook AI Research) · C. Lawrence Zitnick (Facebook AI Research)
Dual Discriminator Generative Adversarial Nets
Tu Nguyen (Deakin University) · Trung Le (Deakin University) · Hung Vu (Deakin University) · Dinh Phung (Deakin University)
Dynamic Revenue Sharing
Santiago Balseiro (Duke University) · Max Lin (Google) · Vahab Mirrokni (Google Research NYC) · Renato Leme (Google Research) · IIIS Song Zuo (IIIS, Tsinghua University)
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Mohammad Ali Bashiri (University of Illinois at Chicago) · Xinhua Zhang (University of Illinois at Chicago)
Multi-agent Predictive Modeling with Attentional CommNets
Yedid Hoshen (Facebook AI Research)
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
James McInerney (Spotify Research)
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Di Wang (State University of New York at Buffalo) · Minwei Ye (University at Buffalo) · Jinhui Xu (SUNY at Buffalo)
Variational Inference via $\chi$ Upper Bound Minimization
Adji Bousso Dieng (Columbia University) · Dustin Tran (Columbia University & OpenAI) · Rajesh Ranganath (Princeton University) · John Paisley () · David Blei (Columbia University)
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li (University of Minnesota) · Lin Yang () · Jason Ge (Princeton University) · Jarvis Haupt (University of Minnesota) · Tong Zhang (Tencent AI Lab) · Tuo Zhao (Georgia Tech)
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang (UC Berkeley) · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Davis Foote (Google Brain) · Yan Duan () · OpenAI Xi Chen (OpenAI, UC Berkeley) · Rein Houthooft (OpenAI) · Adam Stooke (UC Berkeley) · Filip DeTurck ()
An Empirical Study on The Properties of Random Bases for Kernel Methods
Maximilian Alber (TU Berlin) · Pieter-Jan Kindermans (Google Brain Resident) · Kristof Schütt (TU Berlin) · Klaus-Robert Müller (TU Berlin) · Fei Sha (University of Southern California (USC))
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum (Google) · Mohammad Norouzi () · Kelvin Xu (Google) · Dale Schuurmans (Google)
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang (University of Michigan) · Yihe Tang (University of Michigan) · Jian Wang (University of Michigan) · Jia Deng (University of Michigan)
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran (The University of Adelaide) · Trung T Pham (The University of Adelaide) · Gustavo Carneiro (The University of Adelaide) · Lyle Palmer (The University of Adelaide) · Ian Reid (University of Adelaide)
Principles of Riemannian Geometry in Neural Networks
Michael Hauser (Pennsylvania State University) · Asok Ray (Pennsylvania State University)
Cold-Start Reinforcement Learning with Softmax Policy Gradients
Nan Ding (Google) · Radu Soricut (Google)
Online Dynamic Programming
Holakou Rahmanian (University of California at Santa Cruz) · Manfred K Warmuth (Univ. of Calif. at Santa Cruz)
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Sheng Chen (University of Minnesota) · Arindam Banerjee (University of Minnesota)
Convolutional Gaussian Processes
Mark van der Wilk (University of Cambridge) · Carl Edward Rasmussen (University of Cambridge) · James Hensman (PROWLER.io)
Estimation of the covariance structure of heavy-tailed distributions
Xiaohan Wei (University of Southern California) · Stanislav Minsker (USC)
Mean Field Residual Networks: On the Edge of Chaos
Ge Yang (Harvard University)
Decomposable Submodular Function Minimization: Discrete and Continuous
Alina Ene (University of Warwick) · Huy Nguyen (Northeastern University) · Laszlo Vegh (London School of Economics)
Gauging Variational Inference
Sung-Soo Ahn (KAIST) · Michael Chertkov (Los Alamos National Laboratory) · Jinwoo Shin (KAIST)
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Seunghyun Park (Seoul National University) · Seonwoo Min (Seoul National University) · Hyun-soo Choi (Seoul Nation University) · Sungroh Yoon (Seoul National University)
Robust Estimation of Neural Signals in Calcium Imaging
Hakan Inan (Stanford University) · Murat Erdogdu (Microsoft Research) · Mark Schnitzer (Stanford University)
State Aware Imitation Learning
Yannick Schroecker (Georgia Institute of Technology) · Charles L Isbell (Georgia Tech)
Beyond Parity: Fairness Objectives for Collaborative Filtering
Sirui Yao (Virginia Polytechnic Institute and State University) · Bert Huang (Virginia Tech)
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Ben London (Amazon)
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Roel Dobbe (UC Berkeley) · David Fridovich-Keil (UC Berkeley) · Claire Tomlin (UC Berkeley)
Model-Powered Conditional Independence Test
Rajat Sen (University of Texas at Austin) · Ananda Theertha Suresh (Google) · Karthikeyan Shanmugam (IBM Research, NY) · Alexandros Dimakis (University of Texas, Austin) · Sanjay Shakkottai (The University of Texas at Austin)
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Andrew Gibiansky (Baidu Research)
Variance-based Regularization with Convex Objectives
Hongseok Namkoong (Stanford University) · John C Duchi (Stanford)
Deep Lattice Networks and Partial Monotonic Functions
Seungil You (Google) · David Ding (Google) · Kevin Canini (Google) · Jan Pfeifer (Google) · Maya Gupta (Google)
Continual Learning with Deep Generative Replay
Hanul Shin (Massachusetts Institute of Technology) · Jung Kwon Lee (SK T-Brain) · Jaehong Kim (T-Brain) · Jiwon Kim (SK T-Brain)
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco Cusumano-Towner (Massachusetts Institute of Technology) · Vikash K Mansinghka (Massachusetts Institute of Technology)
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami (University of Illinois at Urbana–Champaign) · Saber Salehkaleybar (University of Illinois at Urbana-Champaign) · Negar Kiyavash (UIUC) · Kun Zhang (CMU)
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Zheng Wen (Adobe Research) · Branislav Kveton (Adobe Research) · Michal Valko (Inria Lille - Nord Europe) · Sharan Vaswani (University of British Columbia)
Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
Yasin Abbasi (Adobe Research) · Peter Bartlett (UC Berkeley) · Victor Gabillon (QUT - ACEMS)
Reinforcement Learning under Model Mismatch
Aurko Roy (Google Brain) · Huan Xu () · Sebastian Pokutta (Georgia Institute of Technology)
Hierarchical Attentive Recurrent Tracking
Adam Kosiorek (University of Oxford) · Alex Bewley (University of Oxford) · Ingmar Posner (Oxford University)
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Nicolò Colombo (University College London) · Ricardo Silva (University College London) · Soong Moon Kang (University College London)
Rotting Bandits
Nir Levine (Technion - Israel Institute of Technology) · Koby Crammer (Technion) · Shie Mannor (Technion)
Unbiased estimates for linear regression via volume sampling
Michal Derezinski (UC Santa Cruz) · Manfred K Warmuth (Univ. of Calif. at Santa Cruz)
An Applied Algorithmic Foundation for Hierarchical Clustering
Joshua Wang (Stanford University) · Benjamin Moseley (Washington University in St Lo)
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Mingrui Liu (The University of Iowa) · Tianbao Yang (The University of Iowa)
Stein Variational Gradient Descent as Gradient Flow
Qiang Liu (Dartmouth College)
Partial Hard Thresholding: A Towards Unified Analysis of Support Recovery
Jie Shen (Rutgers University) · Ping Li (Rugters University)
Shallow Updates for Deep Reinforcement Learning
Nir Levine (Technion - Israel Institute of Technology) · Tom Zahavy (The Technion) · Daniel J Mankowitz (Technion) · Aviv Tamar (UC Berkeley) · Shie Mannor (Technion)
A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke (Microsoft Research) · Qi Meng (Peking University) · Taifeng Wang (Microsoft Research) · Wei Chen (Microsoft Research) · Weidong Ma (Microsoft Research) · Tie-Yan Liu (Microsoft Research)
Adversarial Ranking for Language Generation
Dianqi Li (University of Washington) · Kevin Lin (University of Washington) · Xiaodong He (Microsoft Research, Redmond, WA) · Ming-ting Sun (University of Washington) · Zhengyou Zhang (Microsoft Research)
Regret Minimization in MDPs with Options without Prior Knowledge
Ronan Fruit (Inria Lille) · Matteo Pirotta (INRIA Lille-Nord Europe) · Alessandro Lazaric (INRIA Lille-Nord Europe) · Emma Brunskill (CMU)
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi (IBM TJ Watson Research Center) · Nam Nguyen (IBM Thomas J. Watson Research Center) · Justin Romberg (Georgia Institute of Technology)
Graph Matching via Multiplicative Update Algorithm
Bo Jiang (Anhui University) · Jin Tang () · Bin Luo ()
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Qi Lou (UCI) · Rina Dechter (UCI) · Alexander Ihler (UC Irvine)
Is the Bellman residual a bad proxy?
Matthieu Geist (Université de Lorraine) · Bilal Piot (DeepMind) · Olivier Pietquin (DeepMind)
Generalization Properties of Learning with Random Features
Alessandro Rudi (INRIA) · Lorenzo Rosasco (University of Genova- MIT - IIT)
Differentially private Bayesian learning on distributed data
Mikko Heikkilä (University of Helsinki) · Eemil Lagerspetz (University of Helsinki) · Samuel Kaski (Aalto University) · Kana Shimizu (Waseda University) · Sasu Tarkoma (University of Helsinki) · Antti Honkela (University of Helsinki)
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander Ratner (Stanford) · Henry Ehrenberg (Stanford University) · Zeshan Hussain (Stanford University) · Jared Dunnmon (Stanford University) · Christopher Ré (Stanford)
Wasserstein Learning of Deep Generative Point Process Models
SHUAI XIAO (Georgia Institute of Technology) · Mehrdad Farajtabar (Georgia Tech) · Xiaojing Ye (Georgia State University) · Junchi Yan (IBM Research - China) · Le Song (Georgia Institute of Technology) · Hongyuan Zha (Georgia Tech)
Ensemble Sampling
Xiuyuan Lu (Stanford University) · Benjamin Van Roy (Stanford University)
Language modeling with recurrent highway hypernetworks
Joseph Suarez (Stanford University)
Searching in the Dark: Practical SVRG Methods under Error Bound Conditions with Guarantee
Yi Xu (The University of Iowa) · Univ Qihang Lin (Univ Iowa, faculty) · Tianbao Yang (The University of Iowa)
Bayesian Compression for Deep Learning
Christos Louizos (University of Amsterdam) · Karen Ullrich (University of Amsterdam) · Max Welling (University of Amsterdam and University of California Irvine and CIFAR)
Streaming Sparse Gaussian Process Approximations
Thang D Bui (University of Cambridge) · Cuong Nguyen (University of Cambridge) · Richard E Turner (University of Cambridge)
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava (University of Edinburgh) · Lazar Valkoz (University of Edinburgh) · Chris Russell (The Alan Turing Institute/ The University of Surrey) · Michael Gutmann (University of Edinburgh) · Charles Sutton (University of Edinburgh)
Sparse k-Means Embedding
Weiwei Liu (UTS) · Xiaobo Shen (NJUST) · Ivor Tsang (University of Technology, Sydney)
Utile Context Tree Weighting
Joao V Messias (Morpheus Labs) · Shimon Whiteson (Oxford University)
A Regularized Framework for Sparse and Structured Neural Attention
Vlad Niculae (Cornell University) · Mathieu Blondel (NTT)
Multi-output Polynomial Networks and Factorization Machines
Mathieu Blondel (NTT) · Vlad Niculae (Cornell University) · Takuma Otsuka (NTT Communication Science Labs) · Naonori Ueda (NTT Communication Science Laboratories)
Clustering Billions of Reads for DNA Data Storage
Cyrus Rashtchian (University of Washington) · Konstantin Makarychev (Microsoft) · Luis Ceze (Microsoft) · Karin Strauss (Microsoft Research) · Sergey Yekhanin (Microsoft) · Djordje Jevdjic (Microsoft Research) · Miklos Racz (Princeton University) · Siena Ang (Microsoft)
Multi-Objective Non-parametric Sequential Prediction
Guy Uziel (Technion) · Ran El-Yaniv (Technion)
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Ashkan Panahi (North Carolina State University) · Babak Hassibi (Caltech)
Deep Sets
Manzil Zaheer (Carnegie Mellon University) · Satwik Kottur (Carnegie Mellon University) · Siamak Ravanbakhsh (CMU/UBC) · Barnabas Poczos (Carnegie Mellon University) · Ruslan Salakhutdinov () · Alexander Smola (Amazon - We are hiring!)
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
MILA Tegan Maharaj (MILA, Polytechnic Montreal) · Evan Racah () · Christopher J Beckham (Montreal Institute of Learning Algorithms) · Mr. Prabhat (LBL/NERSC) · Chris Pal (Montréal Institute for Learning Algorithms)
Process-constrained batch Bayesian optimisation
Pratibha Vellanki (Deakin University) · Santu Rana (Deakin University) · Sunil Gupta (Deakin University) · David Rubin () · Alessandra Sutti (Deakin University) · Thomas Dorin (Deakin University) · Murray Height () · Paul Sanders () · Svetha Venkatesh (Deakin University)
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed Ibrahim (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)
Spherical convolutions and their application in molecular modelling
Wouter Boomsma (University of Copenhagen) · Jes Frellsen (IT University of Copenhagen)
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Wenbing Huang (Tencent AI Lab) · Fuchun Sun (Tsinghua University) · Tong Zhang (The Australian National University) · Junzhou Huang (University of Texas at Arlington) · Mehrtash Harandi (Data61)
On Optimal Generalizability in Parametric Learning
Ahmad Beirami (Harvard University & MIT) · Meisam Razaviyayn (University of Southern California) · Shahin Shahrampour (Harvard University) · Vahid Tarokh (Harvard University)
Near Optimal Sketching of Low-Rank Tensor Regression