AISTATS 2017
167 papers
A Learning Theory of Ranking Aggregation
Anna Korba, Stéphan Clémençon, Eric Sibony Annular Augmentation Sampling
Francois Fagan, Jalaj Bhandari, John P. Cunningham ASAGA: Asynchronous Parallel SAGA
Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien Attributing Hacks
Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng Automated Inference with Adaptive Batches
Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski Clustering from Multiple Uncertain Experts
Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas Comparison-Based Nearest Neighbor Search
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg Consistent and Efficient Nonparametric Different-Feature Selection
Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi Contextual Bandits with Latent Confounders: An NMF Approach
Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola Discovering and Exploiting Additive Structure for Bayesian Optimization
Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse Distance Covariance Analysis
Benjamin Cowley, João D. Semedo, Amin Zandvakili, Matthew A. Smith, Adam Kohn, Byron M. Yu Distribution of Gaussian Process Arc Lengths
Justin Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts DP-EM: Differentially Private Expectation Maximization
Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling Encrypted Accelerated Least Squares Regression
Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi Fast Classification with Binary Prototypes
Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon Frank-Wolfe Algorithms for Saddle Point Problems
Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien Generalization Error of Invariant Classifiers
Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell Gray-Box Inference for Structured Gaussian Process Models
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain
Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon Hit-and-Run for Sampling and Planning in Non-Convex Spaces
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek Horde of Bandits Using Gaussian Markov Random Fields
Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan Improved Strongly Adaptive Online Learning Using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett Large-Scale Data-Dependent Kernel Approximation
Catalin Ionescu, Alin-Ionut Popa, Cristian Sminchisescu Learning Optimal Interventions
Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola Local Group Invariant Representations via Orbit Embeddings
Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf Local Perturb-and-MAP for Structured Prediction
Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi Localized Lasso for High-Dimensional Regression
Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski Minimax Gaussian Classification & Clustering
Tianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar On the Learnability of Fully-Connected Neural Networks
Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan On the Troll-Trust Model for Edge Sign Prediction in Social Networks
Géraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale Performance Bounds for Graphical Record Linkage
Rebecca C. Steorts, Matt Barnes, Willie Neiswanger Random Consensus Robust PCA
Daniel L. Pimentel-Alarcón, Robert D. Nowak Rank Aggregation and Prediction with Item Features
Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, Eric Vigoda Regression Uncertainty on the Grassmannian
Yi Hong, Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer Regret Bounds for Lifelong Learning
Pierre Alquier, The Tien Mai, Massimiliano Pontil Regret Bounds for Transfer Learning in Bayesian Optimisation
Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh Relativistic Monte Carlo
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer Reparameterization Gradients Through Acceptance-Rejection Sampling Algorithms
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei Scalable Greedy Feature Selection via Weak Submodularity
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh Scalable Learning of Non-Decomposable Objectives
Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan Scaling Submodular Maximization via Pruned Submodularity Graphs
Tianyi Zhou, Hua Ouyang, Jeff A. Bilmes, Yi Chang, Carlos Guestrin Sequential Graph Matching with Sequential Monte Carlo
Seong-Hwan Jun, Samuel W. K. Wong, James V. Zidek, Alexandre Bouchard-Côté Sparse Accelerated Exponential Weights
Pierre Gaillard, Olivier Wintenberger Spatial Decompositions for Large Scale SVMs
Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart Stochastic Rank-1 Bandits
Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen Structured Adaptive and Random Spinners for Fast Machine Learning Computations
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cédric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamás Sarlós, Jamal Atif Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
Andrew Stevens, Yunchen Pu, Yannan Sun, Gregory Spell, Lawrence Carin Trading Off Rewards and Errors in Multi-Armed Bandits
Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu Unsupervised Sequential Sensor Acquisition
Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama