AISTATS 2016

164 papers

One Scan 1-Bit Compressed Sensing

Ping Li

Discriminative Structure Learning of Arithmetic Circuits

Amirmohammad Rooshenas, Daniel Lowd

Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization

Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin

Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees

Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks

Abdullah Rashwan, Han Zhao, Pascal Poupart

Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

Dejiao Zhang, Laura Balzano

Supervised Neighborhoods for Distributed Nonparametric Regression

Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu

Multiresolution Matrix Compression

Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor

Chained Gaussian Processes

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

Spectral M-estimation with Applications to Hidden Markov Models

Dustin Tran, Minjae Kim, Finale Doshi-velez

Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments

Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert

NYTRO: When Subsampling Meets Early Stopping

Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco

Loss Bounds and Time Complexity for Speed Priors

Daniel Filan, Jan Leike, Marcus Hutter

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions

Loic Landrieu, Guillaume Obozinski

Back to the Future: Radial Basis Function Networks Revisited

Qichao Que, Mikhail Belkin

Streaming Kernel Principal Component Analysis

Mina Ghashami, Daniel J. Perry, Jeff Phillips

Active Learning Algorithms for Graphical Model Selection

Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong H. Park

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin

A Fast and Reliable Policy Improvement Algorithm

Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright

Efficient Sampling for k-Determinantal Point Processes

Chengtao Li, Stefanie Jegelka, Suvrit Sra

Parallel Markov Chain Monte Carlo via Spectral Clustering

Guillaume Basse, Aaron Smith, Natesh Pillai

Private Causal Inference

Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger

Communication Efficient Distributed Agnostic Boosting

Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau

Towards Stability and Optimality in Stochastic Gradient Descent

Panos Toulis, Dustin Tran, Edo Airoldi

On Lloyd’s Algorithm: New Theoretical Insights for Clustering in Practice

Cheng Tang, Claire Monteleoni

Robust Covariate Shift Regression

Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart

Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation

Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar

Graph Sparsification Approaches for Laplacian Smoothing

Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani

A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees

Jean-Francis Roy, Mario Marchand, François Laviolette

Controlling Bias in Adaptive Data Analysis Using Information Theory

Daniel Russo, James Zou

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

Hanie Sedghi, Majid Janzamin, Anima Anandkumar

On Searching for Generalized Instrumental Variables

Benito Zander, Maciej Liśkiewicz

Efficient Bregman Projections onto the Permutahedron and Related Polytopes

Cong Han Lim, Stephen J. Wright

Black-Box Policy Search with Probabilistic Programs

Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood

Online Learning with Noisy Side Observations

Tomáš Kocák, Gergely Neu, Michal Valko

Non-Gaussian Component Analysis with Log-Density Gradient Estimation

Hiroaki Sasaki, Gang Niu, Masashi Sugiyama

Unbounded Bayesian Optimization via Regularization

Bobak Shahriari, Alexandre Bouchard-côté, Nando Freitas

Improper Deep Kernels

Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

Consistently Estimating Markov Chains with Noisy Aggregate Data

Garrett Bernstein, Daniel Sheldon

Topic-Based Embeddings for Learning from Large Knowledge Graphs

Changwei Hu, Piyush Rai, Lawrence Carin

Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information

Changwei Hu, Piyush Rai, Lawrence Carin

Scalable and Sound Low-Rank Tensor Learning

Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans

Determinantal Regularization for Ensemble Variable Selection

Veronika Ročková, Gemma Moran, Edward George

Bethe Learning of Graphical Models via MAP Decoding

Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara

Tight Variational Bounds via Random Projections and I-Projections

Lun-Kai Hsu, Tudor Achim, Stefano Ermon

Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, Avinava Dubey, Andrew G. Wilson, Barnabás Póczos, Jeff Schneider

Early Stopping as Nonparametric Variational Inference

David Duvenaud, Dougal Maclaurin, Ryan Adams

Fitting Spectral Decay with the k-Support Norm

Andrew McDonald, Massimiliano Pontil, Dimitris Stamos

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin

Inference for High-dimensional Exponential Family Graphical Models

Jialei Wang, Mladen Kolar

A Convex Surrogate Operator for General Non-Modular Loss Functions

Jiaqian Yu, Matthew Blaschko

Optimization as Estimation with Gaussian Processes in Bandit Settings

Zi Wang, Bolei Zhou, Stefanie Jegelka

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill

Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett

Unsupervised Feature Selection by Preserving Stochastic Neighbors

Xiaokai Wei, Philip S. Yu

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, Le Song

Globally Sparse Probabilistic PCA

Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche

Exponential Stochastic Cellular Automata for Massively Parallel Inference

Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele

AdaDelay: Delay Adaptive Distributed Stochastic Optimization

Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola

Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces

Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian

Pareto Front Identification from Stochastic Bandit Feedback

Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan

Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects

Mário Figueiredo, Robert Nowak

How to Learn a Graph from Smooth Signals

Vassilis Kalofolias

Pseudo-Marginal Slice Sampling

Iain Murray, Matthew Graham

Semi-Supervised Learning with Adaptive Spectral Transform

Hanxiao Liu, Yiming Yang

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games

Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

Christina Heinze, Brian McWilliams, Nicolai Meinshausen

Model-based Co-clustering for High Dimensional Sparse Data

Aghiles Salah, Nicoleta Rogovschi, Mohamed NADIF

Scalable geometric density estimation

Ye Wang, Antonio Canale, David Dunson

Accelerating Online Convex Optimization via Adaptive Prediction

Mehryar Mohri, Scott Yang

Low-Rank Approximation of Weighted Tree Automata

Guillaume Rabusseau, Borja Balle, Shay Cohen

Variational Gaussian Copula Inference

Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvári

Stochastic Neural Networks with Monotonic Activation Functions

Siamak Ravanbakhsh, Barnabás Póczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

Stochastic Variational Inference for the HDP-HMM

Aonan Zhang, San Gultekin, John Paisley

GLASSES: Relieving The Myopia Of Bayesian Optimisation

Javier Gonzalez, Michael Osborne, Neil Lawrence

Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm

Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation

Sebastian Tschiatschek, Josip Djolonga, Andreas Krause

A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models

Rishit Sheth, Roni Khardon

Distributed Multi-Task Learning

Jialei Wang, Mladen Kolar, Nathan Srerbo

A Deep Generative Deconvolutional Image Model

Yunchen Pu, Win Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lahdesmaki

Scalable MCMC for Mixed Membership Stochastic Blockmodels

Wenzhe Li, Sungjin Ahn, Max Welling

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System

Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing

Variational Tempering

Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei

Sequential Inference for Deep Gaussian Process

Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel Urtasun

Approximate Inference Using DC Programming For Collective Graphical Models

Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Probabilistic Approximate Least-Squares

Simon Bartels, Philipp Hennig

Fast and Scalable Structural SVM with Slack Rescaling

Heejin Choi, Ofer Meshi, Nathan Srebro

Learning Relationships between Data Obtained Independently

Alexandra Carpentier, Teresa Schlueter

Nonparametric Budgeted Stochastic Gradient Descent

Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Phung

Batch Bayesian Optimization via Local Penalization

Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence

New Resistance Distances with Global Information on Large Graphs

Canh Hao Nguyen, Hiroshi Mamitsuka

Fast Dictionary Learning with a Smoothed Wasserstein Loss

Antoine Rolet, Marco Cuturi, Gabriel Peyré

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization

Fanhua Shang, Yuanyuan Liu, James Cheng

NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning

Eli Meirom, Pavel Kisilev

Low-Rank and Sparse Structure Pursuit via Alternating Minimization

Quanquan Gu, Zhaoran Wang Wang, Han Liu