AISTATS 2013

71 papers

A Competitive Test for Uniformity of Monotone Distributions Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

A Last-Step Regression Algorithm for Non-Stationary Online Learning Edward Moroshko, Koby Crammer

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-High Dimensions Prabhanjan Kambadur, Aurélie C. Lozano

A Recursive Estimate for the Predictive Likelihood in a Topic Model James Scott, Jason Baldridge

A Simple Criterion for Controlling Selection Bias Eunice Yuh-Jie Chen, Judea Pearl

A Simple Sketching Algorithm for Entropy Estimation over Streaming Data Peter Clifford, Ioana Cosma

A Unifying Representation for a Class of Dependent Random Measures Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson

Active Learning for Interactive Visualization Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani

Bayesian Learning of Joint Distributions of Objects Anjishnu Banerjee, Jared Murray, David B. Dunson

Bayesian Structure Learning for Functional Neuroimaging Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow

Bethe Bounds and Approximating the Global Optimum Adrian Weller, Tony Jebara

Beyond Sentiment: The Manifold of Human Emotions Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan A. Essa

Block Regularized Lasso for Multivariate Multi-Response Linear Regression Weiguang Wang, Yingbin Liang, Eric P. Xing

Central Limit Theorems for Conditional Markov Chains Mathieu Sinn, Bei Chen

Changepoint Detection over Graphs with the Spectral Scan Statistic James Sharpnack, Aarti Singh, Alessandro Rinaldo

Clustered Support Vector Machines Quanquan Gu, Jiawei Han

Clustering Oligarchies Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato

Collapsed Variational Bayesian Inference for Hidden Markov Models Pengyu Wang, Phil Blunsom

Competing with an Infinite Set of Models in Reinforcement Learning Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner

Completeness Results for Lifted Variable Elimination Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel

Computing the M Most Probable Modes of a Graphical Model Chao Chen, Vladimir Kolmogorov, Yan Zhu, Dimitris N. Metaxas, Christoph H. Lampert

Consensus Ranking with Signed Permutations Raman Arora, Marina Meila

Convex Collective Matrix Factorization Guillaume Bouchard, Dawei Yin, Shengbo Guo

Data-Driven Covariate Selection for Nonparametric Estimation of Causal Effects Doris Entner, Patrik O. Hoyer, Peter Spirtes

Deep Gaussian Processes Andreas C. Damianou, Neil D. Lawrence

Detecting Activations over Graphs Using Spanning Tree Wavelet Bases James Sharpnack, Aarti Singh, Akshay Krishnamurthy

Diagonal Orthant Multinomial Probit Models James E. Johndrow, David B. Dunson, Kristian Lum

Distributed and Adaptive Darting Monte Carlo Through Regenerations Sungjin Ahn, Yutian Chen, Max Welling

Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods Zhaoshi Meng, Dennis L. Wei, Ami Wiesel, Alfred O. Hero Iii

Distribution-Free Distribution Regression Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman

DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes Abner Guzmán-Rivera, Pushmeet Kohli, Dhruv Batra

Dual Decomposition for Joint Discrete-Continuous Optimization Christopher Zach

DYNACARE: Dynamic Cardiac Arrest Risk Estimation Joyce C. Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh

Dynamic Copula Networks for Modeling Real-Valued Time Series Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan

Dynamic Scaled Sampling for Deterministic Constraints Lei Li, Bharath Ramsundar, Stuart Russell

Efficient Variational Inference for Gaussian Process Regression Networks Trung V. Nguyen, Edwin V. Bonilla

Efficiently Sampling Probabilistic Programs via Program Analysis Arun Tejasvi Chaganty, Aditya V. Nori, Sriram K. Rajamani

Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling Jianzhu Ma, Jian Peng, Sheng Wang, Jinbo Xu

Evidence Estimation for Bayesian Partially Observed MRFs Yutian Chen, Max Welling

Exact Learning of Bounded Tree-Width Bayesian Networks Janne H. Korhonen, Pekka Parviainen

Fast Near-GRID Gaussian Process Regression Yuancheng Luo, Ramani Duraiswami

Further Optimal Regret Bounds for Thompson Sampling Shipra Agrawal, Navin Goyal

Greedy Bilateral Sketch, Completion & Smoothing Tianyi Zhou, Dacheng Tao

High-Dimensional Inference via Lipschitz Sparsity-Yielding Regularizers Zheng Pan, Changshui Zhang

Learning Markov Networks with Arithmetic Circuits Daniel Lowd, Amirmohammad Rooshenas

Learning Social Infectivity in Sparse Low-Rank Networks Using Multi-Dimensional Hawkes Processes Ke Zhou, Hongyuan Zha, Le Song

Learning to Top-K Search Using Pairwise Comparisons Brian Eriksson

Localization and Adaptation in Online Learning Alexander Rakhlin, Ohad Shamir, Karthik Sridharan

Meta-Transportability of Causal Effects: A Formal Approach Elias Bareinboim, Judea Pearl

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction Georg M. Goerg, Cosma Rohilla Shalizi

Nystrom Approximation for Large-Scale Determinantal Processes Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox, Ben Taskar

ODE Parameter Inference Using Adaptive Gradient Matching with Gaussian Processes Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone

On the Asymptotic Optimality of Maximum Margin Bayesian Networks Sebastian Tschiatschek, Franz Pernkopf

Permutation Estimation and Minimax Rates of Identifiability Olivier Collier, Arnak S. Dalalyan

Predictive Correlation Screening: Application to Two-Stage Predictor Design in High Dimension Hamed Firouzi, Bala Rajaratnam, Alfred O. Hero Iii

Random Projections for Support Vector Machines Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas

Reconstructing Ecological Networks with Hierarchical Bayesian Regression and Mondrian Processes Andrej Aderhold, Dirk Husmeier, V. Anne Smith

Recursive Karcher Expectation Estimators and Geometric Law of Large Numbers Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri

Scoring Anomalies: A M-Estimation Formulation Stéphan Clémençon, Jérémie Jakubowicz

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series Zhaoran Wang, Fang Han, Han Liu

Statistical Tests for Contagion in Observational Social Network Studies Greg Ver Steeg, Aram Galstyan

Stochastic Blockmodeling of Relational Event Dynamics Christopher DuBois, Carter T. Butts, Padhraic Smyth

Structural Expectation Propagation (SEP): Bayesian Structure Learning for Networks with Latent Variables Nevena Lazic, Christopher M. Bishop, John M. Winn

Structure Learning of Mixed Graphical Models Jason D. Lee, Trevor Hastie

Supervised Sequential Classification Under Budget Constraints Kirill Trapeznikov, Venkatesh Saligrama

Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions Heng Luo, Pierre Luc Carrier, Aaron C. Courville, Yoshua Bengio

Thompson Sampling in Switching Environments with Bayesian Online Change Detection Joseph Charles Mellor, Jonathan Shapiro

Ultrahigh Dimensional Feature Screening via RKHS Embeddings Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Guy Lebanon

Uncover Topic-Sensitive Information Diffusion Networks Nan Du, Le Song, Hyenkyun Woo, Hongyuan Zha

Unsupervised Link Selection in Networks Quanquan Gu, Charu C. Aggarwal, Jiawei Han

Why Steiner-Tree Type Algorithms Work for Community Detection Mung Chiang, Henry Lam, Zhenming Liu, H. Vincent Poor