AISTATS 2016
164 papers
A Deep Generative Deconvolutional Image Model
Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin A Fast and Reliable Policy Improvement Algorithm
Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright A Linearly-Convergent Stochastic L-BFGS Algorithm
Philipp Moritz, Robert Nishihara, Michael I. Jordan A PAC RL Algorithm for Episodic POMDPs
Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill Active Learning Algorithms for Graphical Model Selection
Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park Batch Bayesian Optimization via Local Penalization
Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence Bayesian Generalised Ensemble Markov Chain Monte Carlo
Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg Bayesian Markov Blanket Estimation
Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth Bayesian Nonparametric Kernel-Learning
Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing Bethe Learning of Graphical Models via MAP Decoding
Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara Bipartite Correlation Clustering: Maximizing Agreements
Megasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis Black-Box Policy Search with Probabilistic Programs
Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood Breaking Sticks and Ambiguities with Adaptive Skip-Gram
Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov Chained Gaussian Processes
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence Communication Efficient Distributed Agnostic Boosting
Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau Convex Block-Sparse Linear Regression with Expanders - Provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing Distributed Multi-Task Learning
Jialei Wang, Mladen Kolar, Nathan Srebro Exponential Stochastic Cellular Automata for Massively Parallel Inference
Manzil Zaheer, Michael L. Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr. Fast Convergence of Online Pairwise Learning Algorithms
Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou Fitting Spectral Decay with the K-Support Norm
Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos Geometry Aware Mappings for High Dimensional Sparse Factors
Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Narayan Bhaskar, Suju Rajan GLASSES: Relieving the Myopia of Bayesian Optimisation
Javier González, Michael A. Osborne, Neil D. Lawrence Globally Sparse Probabilistic PCA
Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche Graph Sparsification Approaches for Laplacian Smoothing
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani Improper Deep Kernels
Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett Latent Point Process Allocation
Chris M. Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts, Tom Nickson Low-Rank Approximation of Weighted Tree Automata
Guillaume Rabusseau, Borja Balle, Shay B. Cohen Multi-Level Cause-Effect Systems
Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona Multiresolution Matrix Compression
Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor Nearly Optimal Classification for Semimetrics
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch No Regret Bound for Extreme Bandits
Robert Nishihara, David Lopez-Paz, Léon Bottou Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki Nonparametric Budgeted Stochastic Gradient Descent
Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung NYTRO: When Subsampling Meets Early Stopping
Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco PAC-Bayesian Bounds Based on the Rényi Divergence
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy Pareto Front Identification from Stochastic Bandit Feedback
Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan Private Causal Inference
Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger Pseudo-Marginal Slice Sampling
Iain Murray, Matthew M. Graham Quantization Based Fast Inner Product Search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha Random Forest for the Contextual Bandit Problem
Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot Robust Covariate Shift Regression
Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart Scalable and Sound Low-Rank Tensor Learning
Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing Scalable Geometric Density Estimation
Ye Wang, Antonio Canale, David B. Dunson Sequential Inference for Deep Gaussian Process
Yali Wang, Marcus A. Brubaker, Brahim Chaib-draa, Raquel Urtasun Simple and Scalable Constrained Clustering: A Generalized Spectral Method
Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, Richard Peng Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces
Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner Streaming Kernel Principal Component Analysis
Mina Ghashami, Daniel J. Perry, Jeff M. Phillips Survey Propagation Beyond Constraint Satisfaction Problems
Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey The Nonparametric Kernel Bayes Smoother
Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song Time-Varying Gaussian Process Bandit Optimization
Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas Unsupervised Ensemble Learning with Dependent Classifiers
Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger Variational Gaussian Copula Inference
Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin Variational Tempering
Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei