Microsoft @ NIPS 2017

“A Decomposition of Forecast Error in Prediction Markets” by Miro Dudik, Sebastien Lahaie, Ryan M Rogers, and Jennifer Wortman Vaughan

“A Highly Efficient Gradient Boosting Decision Tree” by Guolin Ke, Qi Meng, Taifeng Wang, Wei Chen, Weidong Ma, and Tie-Yan Liu

“A Sample Complexity Measure with Applications to Learning Optimal Auctions” by Vasilis Syrgkanis

“Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM” by Steven Wu, Bo Waggoner, Seth Neel, Aaron Roth, and Katrina Ligett

“Adversarial Ranking for Language Generation” by Dianqi Li, Kevin Lin, Xiaodong He, Ming-ting Sun, and Zhengyou Zhang

“Clustering Billions of Reads for DNA Data Storage” by Cyrus Rashtchian, Konstantin Makarychev, Luis Ceze, Karin Strauss, Sergey Yekhanin, Djordje Jevdjic, Miklos Racz, and Siena Ang

“Collecting Telemetry Data Privately” by Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin

“Consistent Robust Regression” by Kush Bhatia, Prateek Jain, and Purushottam Kar

“Decoding with Value Networks for Neural Machine Translation” by Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, and Tieyan Liu

“Deliberation Networks: Sequence Generation Beyond One-Pass Decoding” by Yingce Xia, Lijun Wu, Jianxin Lin, Fei Tian, Tao Qin, and Tie-Yan Liu

“Efficiency Guarantees from Data” by Darrell Hoy, Tremor Technologies; Denis Nekipelov, University of Virginia; and Vasilis Syrgkanis, Microsoft Research

“Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach” by Emmanouil Platanios, Carnegie Mellon University; Hoifung Poon, Microsoft Research; Tom M. Mitchell, Carnegie Mellon University; and Eric J. Horvitz, Microsoft Research

“From Bayesian Sparsity to Gated Recurrent Nets” by Hao He, Massachusetts Institute of Technology; Bo Xin, Microsoft Research; and David Wipf, Microsoft Research

“Hybrid Reward Architecture for Reinforcement Learning” by Harm Van Seijen, Microsoft Research; Romain Laroche, Microsoft Research, Maluuba; Mehdi Fatemi, Microsoft Research; and Joshua Romoff, McGill University

“Identifying Outlier Arms in Multi-Armed Bandit” by Honglei Zhuang, University of Illinois; Chi Wang, Microsoft Research; and Yifan Wang, Tsinghua University

“Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications” by Qinshi Wang and Wei Chen

“Inference in Graphical Models via Semidefinite Programming Hierarchies” by Murat Erdogdu, Yash Deshpande, and Andrea Montanari

“Influence Maximization with ε-Almost Submodular Threshold Function” by Qiang Li, Institute of Computing Technol; Wei Chen, Microsoft Research; Xiaoming Sun, Institute of Computing Technology, Chinese Academy of Sciences; and Jialin Zhang, Institute of Computing Technology, Chinese Academy of Sciences

“Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences” by Kinjal Basu, Ankan Saha, and Shaunak Chatterjee, LinkedIn Corporation

“Learning Mixture of Gaussians with Streaming Data” by Aditi Raghunathan, Stanford University; Prateek Jain, Microsoft Research; and Ravishankar Krishnawamy, Microsoft Research

“Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls” by Zeyuan Allen-Zhu, Microsoft Research; Elad Hazan, Princeton University; Wei Hu, Princeton University; Yuanzhi Li, Princeton University

“The Importance of Communities for Learning to Influence” by Eric Balkanski, Harvard University; Nicole Immorlica, Microsoft Research; and Yaron Singer, Harvard University

“Mean Field Residual Networks: On the Edge of Chaos” by Greg Yang, Microsoft Research; Samuel S. Schoenholz, Google Brain

“Multi-Task Learning for Contextual Bandits” by Aniket Anand Deshmukh, University of Michigan, Ann Arbor; Urun Dogan, Microsoft; and Clay Scott, University of Michigan

“Neural Program Meta-Induction” by Jacob Devlin, Microsoft Research; Rudy R Bunel, Oxford University; Rishabh Singh, Microsoft Research; Matthew Hausknecht, Microsoft Research; and Pushmeet Kohli, DeepMind

“Non-convex Robust PCA” by Praneeth Netrapalli, Microsoft Research; Niranjan Uma Naresh, UC Irvine; Sujay Sanghavi, UT-Austin; Animashree Anadkumar, UC-Irvine; Prateek Jain, Microsoft Research

“Off-policy Evaluation for Slate Recommendation” by Adith Swaminathan, Microsoft Research; Akshay Krishnamurthy, University of Massachusetts; Alekh Agarwal, Microsoft Research; Miro Dudik, Microsoft Research; John Langford, Microsoft Research; Damien Jose, Microsoft; and Imed Zitouni, Microsoft Research

“Online Learning with a Hint” by Ofer Dekel, Microsoft Research; Arthur Flajolet, Massachusetts Institute of Technology; Nika Haghtalab, Carnegie Mellon University; and Patrick Jaillet, Massachusetts Institute of Technology

“Plan, Attend, Generate: Planning for Sequence-to-Sequence Models” by Caglar Gulcehre, Deepmind; Francis Dutil, MILA; Adam Trischler, Microsoft; and Yoshua Bengio, University of Montreal

“Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes” by Jianshu Chen, Microsoft Research; Chong Wang, Princeton University; Lin Xiao, Microsoft Research; Ji He, University Washington; Lihong Li, Microsoft Research; and Li Deng, Citadel

“QSGD: Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks” by Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic

“Repeated Inverse Reinforcement Learning” by Kareem Amin, Google Research; Nan Jiang, Microsoft Research; and Satinder Singh, University of Michigan

“Robust Estimation of Neural Signals in Calcium Imaging” by Hakan Inan, Stanford University; Murat Erdogdu, Microsoft Research; and Mark Schnitzer, Stanford University

“Robust Optimization for Non-Convex Objectives” by Yaron Singer, Harvard University; Robert S Chen, Harvard University; Vasilis Syrgkanis, Microsoft Research; and Brendan Lucier, Microsoft Research

“Stabilizing Training of Generative Adversarial Networks through Regularization” by Kevin Roth, ETH; Aurelien Lucchi, ETH Zurich; Sebastian Nowozin, Microsoft Research; and Thomas Hofmann, ETH Zurich

“Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues” by 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; and Amir Yehudayoff, Technion – Israel Institute of Technology

“The Numerics of GANs” by Lars Mescheder, Max-Planck Institute Tuebingen; Sebastian Nowozin, Microsoft Research; and Andreas Geiger, MPI Tübingen

“Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Microsoft Research; Jennifer Chayes, Microsoft Research; Christina Lee, Microsoft Research; and Devavrat Shah, Massachusetts Institute of Technology

“Unsupervised Sequence Classification using Sequential Output Statistics” by Yu Liu, SUNY Buffalo; Jianshu Chen, Microsoft Research; and Li Deng, Citadel

“Z-Forcing: Training Stochastic Recurrent Networks” by Marc-Alexandre Côté, Microsoft Research; Alessandro Sordoni, Microsoft Research, Maluuba; Anirudh Goyal, Université de Montréal; Nan Ke, MILA, École Polytechnique de Montréal; and Yoshua Bengio, University of Montreal