Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS) · Yuanzhi Li (Princeton University)
Follow the Compressed Leader: Even Faster Online Learning of Eigenvectors
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS) · Yuanzhi Li (Princeton University)
Faster Principal Component Regression via Optimal Polynomial Approximation to Matrix sgn(x)
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS) · Yuanzhi Li (Princeton University)
Sequence Modeling via Segmentations
Chong Wang (Microsoft Research) · Yining Wang (CMU) · Po-Sen Huang (Microsoft Research) · Abdelrahman Mohammad (Microsoft) · Dengyong Zhou (Microsoft Research) · Li Deng (Citadel)
Measuring Sample Quality with Kernels
Jackson Gorham (STANFORD) · Lester Mackey (Microsoft Research)
Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng (University of Science and Technology of China) · Qi Meng (Peking University) · Taifeng Wang (Microsoft Research) · Wei Chen (Microsoft Research) · Tie-Yan Liu (Microsoft)
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS)
Near-Optimal Design of Experiments via Regret Minimization
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS) · Yuanzhi Li (Princeton University) · Aarti Singh () · Yining Wang (CMU)
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang (Microsoft Research) · Akshay Krishnamurthy (UMass) · Alekh Agarwal (Microsoft Research) · John Langford (Microsoft Research) · Robert Schapire (Microsoft Research)
Logarithmic Time One-Against-Some
Hal Daumé (University of Maryland) · NIKOS KARAMPATZIAKIS (Microsoft) · John Langford (Microsoft Research) · Paul Mineiro (Microsoft)
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang (Carnegie Mellon University / Amazon AWS) · Alekh Agarwal (Microsoft Research) · Miroslav Dudik (Microsoft Research)
Safety-Aware Algorithms for Adversarial Contextual Bandit
Wen Sun (Carnegie Mellon University) · Debadeepta Dey (Microsoft) · Ashish Kapoor (Microsoft Research)
How to Escape Saddle Points Efficiently
Chi Jin (UC Berkeley) · Rong Ge (Duke University) · Praneeth Netrapalli (Microsoft Research) · Sham M. Kakade (University of Washington) · Michael Jordan ()
Stochastic Variance Reduction Methods for Policy Evaluation
Simon Du (Carnegie Mellon University) · Jianshu Chen (Microsoft Research) · Lihong Li (Microsoft Research) · Lin Xiao (Microsoft Research) · Dengyong Zhou (Microsoft Research)
Provable Optimal Algorithms for Generalized Linear Contextual Bandits
Lihong Li (Microsoft Research) · Yu Lu (Yale University) · Dengyong Zhou (Microsoft Research)
Learning Continuous Semantic Representations of Symbolic Expressions
Miltiadis Allamanis (Microsoft Research) · pankajan Chanthirasegaran () · Pushmeet Kohli (Microsoft Research) · Charles Sutton (University of Edinburgh)
RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin (Microsoft Research) · Jonathan Uesato (MIT) · Surya Bhupatiraju (MIT) · Rishabh Singh (Microsoft Research) · Abdelrahman Mohammad (Microsoft) · Pushmeet Kohli (Microsoft Research)
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Lars Mescheder (MPI Tübingen) · Sebastian Nowozin (Microsoft Research) · Andreas Geiger (MPI Tübingen)
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Chirag Gupta (Microsoft Research, India) · ARUN SUGGALA (Carnegie Mellon University) · Ankit Goyal (University of Michigan) · Saurabh Goyal (IBM India Pvt Ltd) · Ashish Kumar (Microsoft Research) · Bhargavi Paranjape (Microsoft Research) · Harsha Vardhan Simhadri (Microsoft Research) · Raghavendra Udupa (Microsoft Research) · Manik Varma (Microsoft Research) · Prateek Jain (Microsoft Research)
Optimal algorithms for smooth and strongly convex distributed optimization in networks
Kevin Scaman (MSR-INRIA Joint Center) · Yin Tat Lee (Microsoft Research) · Francis Bach (INRIA) · Sebastien Bubeck (Microsoft Research) · Laurent Massoulié (MSR-INRIA Joint Center)
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
Ashish Kumar (Microsoft Research) · Saurabh Goyal (IBM India Pvt Ltd) · Manik Varma (Microsoft Research)
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Zi Wang (MIT) · Chengtao Li () · Stefanie Jegelka (MIT) · Pushmeet Kohli (Microsoft Research)
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong (University of Texas at Austin) · Zhao Song (UT-Austin) · Prateek Jain (Microsoft Research) · Peter Bartlett (UC Berkeley) · Inderjit Dhillon (UT Austin & Amazon)
Dual Supervised Learning
Yingce Xia (University of Science and Technology of China) · Tao Qin (Microsoft Research Asia) · Wei Chen (Microsoft Research) · Jiang Bian (Microsoft Research) · Nenghai Yu (USTC) · Tie-Yan Liu (Microsoft)
Improving Gibbs Sampler Scan Quality with DoGS
Ioannis Mitliagkas (Stanford University) · Lester Mackey (Microsoft Research)
Nearly Optimal Robust Matrix Completion
Yeshwanth Cherapanamjeri (Microsoft Research) · Prateek Jain (Microsoft Research) · Kartik Gupta (Microsoft Research)
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster (University of Oxford) · Nantas Nardelli (University of Oxford) · Gregory Farquhar (University of Oxford) · Phil Torr (Oxford) · Pushmeet Kohli (Microsoft Research) · Shimon Whiteson (University of Oxford)
Differentiable Programs with Neural Libraries
Alex Gaunt (Microsoft) · Marc Brockschmidt (Microsoft Research) · Nate Kushman (Microsoft Research) · Daniel Tarlow (Google Brain)
Active Heteroscedastic Regression
Kamalika Chaudhuri (University of California at San Diego) · Prateek Jain (Microsoft Research) · Nagarajan Natarajan (Microsoft Research)
Consistency Analysis for Binary Classification Revisited
Wojciech Kotlowski (Poznan University of Technology) · Nagarajan Natarajan (Microsoft Research) · Krzysztof Dembczynski (Poznan University of Technology) · Oluwasanmi Koyejo (University of Illinois at Urbana-Champaign)
Active Learning for Cost-Sensitive Classification
Alekh Agarwal (Microsoft Research) · Akshay Krishnamurthy (UMass) · Tzu-Kuo Huang (Uber) · Hal Daumé III (University of Maryland) · John Langford (Microsoft Research)
Adaptive Neural Networks for Fast Test-Time Prediction
Tolga Bolukbasi (Boston University) · Joseph Wang (Amazon) · Ofer Dekel (Microsoft) · Venkatesh Saligrama (Boston University)
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh (University of Michigan) · Satinder Singh (University of Michigan) · Honglak Lee (Google / U. Michigan) · Pushmeet Kohli (Microsoft Research)
Robust Structured Estimation with Single-Index Models
Sheng Chen (University of Minnesota) · Arindam Banerjee (University of Minnesota) · Sreangsu Acharyya (Microsoft Research India)
Gradient Coding: Avoiding Stragglers in Distributed Learning
Rashish Tandon (University of Texas at Austin) · Qi Lei (University of Texas at Austin) · Alexandros Dimakis (UT Austin) · NIKOS KARAMPATZIAKIS (Microsoft)
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Jialei Wang (University of Chicago) · Lin Xiao (Microsoft Research)
Gradient Boosted Decision Trees for High Dimensional Sparse Output
Si Si (Google Research) · Huan Zhang (UC Davis) · Sathiya Keerthi (Microsoft) · Dhruv Mahajan (Facebook) · Inderjit Dhillon (UT Austin & Amazon) · Cho-Jui Hsieh (University of California, Davis)
Learning Algorithms for Active Learning
Philip Bachman (Maluuba) · Alessandro Sordoni (Microsoft Maluuba) · Adam Trischler (Maluuba)
Deep IV: A Flexible Approach for Counterfactual Prediction
Greg Lewis (Microsoft Research) · Matt Taddy (MICROSOFT) · Jason Hartford (University of British Columbia) · Kevin Leyton-Brown ()