Papers
Exponential Integration for Hamiltonian Monte Carlo
Wei-Lun Chao, Justin Solomon, Dominik Michels et al.
Simple regret for infinitely many armed bandits
Alexandra Carpentier, Michal Valko
Swept Approximate Message Passing for Sparse Estimation
Andre Manoel, Florent Krzakala, Eric Tramel et al.
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Zheng Wen, Branislav Kveton, Azin Ashkan
Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems
Qiang Zhou, Qi Zhao
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech, Jonathan Huang, Andy Nguyen et al.
On Deep Multi-View Representation Learning
Weiran Wang, Raman Arora, Karen Livescu et al.
A Probabilistic Model for Dirty Multi-task Feature Selection
Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani
Learning Fast-Mixing Models for Structured Prediction
Jacob Steinhardt, Percy Liang
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing
Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen et al.
Reified Context Models
Jacob Steinhardt, Percy Liang
Following the Perturbed Leader for Online Structured Learning
Alon Cohen, Tamir Hazan
Finding Galaxies in the Shadows of Quasars with Gaussian Processes
Roman Garnett, Shirley Ho, Jeff Schneider
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone, Raphael Engler
The Ladder: A Reliable Leaderboard for Machine Learning Competitions
Avrim Blum, Moritz Hardt
Safe Exploration for Optimization with Gaussian Processes
Yanan Sui, Alkis Gotovos, Joel Burdick et al.
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
Ching-Pei Lee, Dan Roth
Inferring Graphs from Cascades: A Sparse Recovery Framework
Jean Pouget-Abadie, Thibaut Horel
Bayesian and Empirical Bayesian Forests
Taddy Matthew, Chun-Sheng Chen, Jun Yu et al.
From Word Embeddings To Document Distances
Matt Kusner, Yu Sun, Nicholas Kolkin et al.
Rademacher Observations, Private Data, and Boosting
Richard Nock, Giorgio Patrini, Arik Friedman
Support Matrix Machines
Luo Luo, Yubo Xie, Zhihua Zhang et al.
A Nearly-Linear Time Framework for Graph-Structured Sparsity
Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
Differentially Private Bayesian Optimization
Matt Kusner, Jacob Gardner, Roman Garnett et al.
Large-scale log-determinant computation through stochastic Chebyshev expansions
Insu Han, Dmitry Malioutov, Jinwoo Shin