AISTATS 2016

164 papers

Enumerating Equivalence Classes of Bayesian Networks using EC Graphs

Eunice Yuh-Jie Chen, Arthur Choi Choi, Adnan Darwiche

Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines

Muneki Yasuda

Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings

Zhitang Chen, Pascal Poupart, Yanhui Geng

Universal Models of Multivariate Temporal Point Processes

Asela Gunawardana, Chris Meek

The Nonparametric Kernel Bayes Smoother

Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song

Graph Connectivity in Noisy Sparse Subspace Clustering

Yining Wang, Yu-Xiang Wang, Aarti Singh

Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models

Calvin McCarter, Seyoung Kim

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

Sujith Ravi, Qiming Diao

A PAC RL Algorithm for Episodic POMDPs

Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill

Learning Structured Low-Rank Representation via Matrix Factorization

Jie Shen, Ping Li

An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

Quantization based Fast Inner Product Search

Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha

Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA

Chun-Liang Li, Hsuan-tien Lin, Chi-Jen Lu

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu

Geometry Aware Mappings for High Dimensional Sparse Factors

Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan

Simple and Scalable Constrained Clustering: a Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary Miller, Richard Peng

PAC-Bayesian Bounds based on the Rényi Divergence

Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

Optimal Statistical and Computational Rates for One Bit Matrix Completion

Renkun Ni, Quanquan Gu

A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning

Yan Li, Han Liu, Warren Powell

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings

Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

Latent Point Process Allocation

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson

Nearly Optimal Classification for Semimetrics

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Deep Kernel Learning

Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

Multi-Level Cause-Effect Systems

Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona

Unsupervised Ensemble Learning with Dependent Classifiers

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen

Bayesian Markov Blanket Estimation

Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz

Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

Weici Hu, Peter Frazier

Time-Varying Gaussian Process Bandit Optimization

Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher

CRAFT: ClusteR-specific Assorted Feature selecTion

Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola

Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models

Balázs Csanád Csáji

Survey Propagation beyond Constraint Satisfaction Problems

Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey

Online Learning to Rank with Feedback at the Top

Sougata Chaudhuri, Ambuj Tewari Tewari

Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations

Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan

No Regret Bound for Extreme Bandits

Robert Nishihara, David Lopez-paz, Léon Bottou

A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz, Robert Nishihara, Michael Jordan

Non-stochastic Best Arm Identification and Hyperparameter Optimization

Kevin Jamieson, Ameet Talwalkar

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes

Alexander G. de G. Matthews, James Hensman, Richard Turner, Zoubin Ghahramani

Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables

Yaniv Tenzer, Gal Elidan

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön

Fast Convergence of Online Pairwise Learning Algorithms

Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

Atsushi Nitanda

On the Reducibility of Submodular Functions

Jincheng Mei, Hao Zhang, Bao-Liang Lu

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates

Lingxiao Wang, Xiang Ren, Quanquan Gu

Scalable Gaussian Process Classification via Expectation Propagation

Daniel Hernández-lobato, José Miguel Hernández-Lobato

Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models

Lee H. Dicker, Murat A. Erdogdu

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Jonathan Scarlett, Volkan Cevher

Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls

Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu

Breaking Sticks and Ambiguities with Adaptive Skip-gram

Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov

Bipartite Correlation Clustering: Maximizing Agreements

Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis

Learning Sparse Additive Models with Interactions in High Dimensions

Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics

Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard

Random Forest for the Contextual Bandit Problem

Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot

A Robust-Equitable Copula Dependence Measure for Feature Selection

Yale Chang, Yi Li, Adam Ding, Jennifer Dy

Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking

Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Probability Inequalities for Kernel Embeddings in Sampling without Replacement

Markus Schneider

Control Functionals for Quasi-Monte Carlo Integration

Chris Oates, Mark Girolami

Tightness of LP Relaxations for Almost Balanced Models

Adrian Weller, Mark Rowland, David Sontag

Clamping Improves TRW and Mean Field Approximations

Adrian Weller, Justin Domke

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman

Convex Block-sparse Linear Regression with Expanders – Provably

Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre

Revealing Graph Bandits for Maximizing Local Influence

Alexandra Carpentier, Michal Valko

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Mario Lucic, Olivier Bachem, Andreas Krause