Volume 31: Artificial Intelligence and Statistics, 29-1 May 2013, Scottsdale, Arizona, USA

[edit]

Editors: Carlos M. Carvalho, Pradeep Ravikumar

[bib][citeproc]

Contents:

Part I: Notable Papers

Bayesian learning of joint distributions of objects

Anjishnu Banerjee, Jared Murray, David Dunson ; PMLR 31:1-9

Permutation estimation and minimax rates of identifiability

Olivier Collier, Arnak Dalalyan ; PMLR 31:10-19

A unifying representation for a class of dependent random measures

Nicholas Foti, Joseph Futoma, Daniel Rockmore, Sinead Williamson ; PMLR 31:20-28

Diagonal Orthant Multinomial Probit Models

James Johndrow, David Dunson, Kristian Lum ; PMLR 31:29-38

Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods

Zhaoshi Meng, Dennis Wei, Ami Wiesel, Alfred Hero III ; PMLR 31:39-47

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series

Zhaoran Wang, Fang Han, Han Liu ; PMLR 31:48-56

Part II: Regular Papers

A Competitive Test for Uniformity of Monotone Distributions

Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Suresh ; PMLR 31:57-65

Clustering Oligarchies

Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato ; PMLR 31:66-74

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Andrej Aderhold, Dirk Husmeier, V. Anne Smith ; PMLR 31:75-84

Nystrom Approximation for Large-Scale Determinantal Processes

Raja Hafiz Affandi, Alex Kulesza, Emily Fox, Ben Taskar ; PMLR 31:85-98

Further Optimal Regret Bounds for Thompson Sampling

Shipra Agrawal, Navin Goyal ; PMLR 31:99-107

Distributed and Adaptive Darting Monte Carlo through Regenerations

Sungjin Ahn, Yutian Chen, Max Welling ; PMLR 31:108-116

Consensus Ranking with Signed Permutations

Raman Arora, Marina Meila ; PMLR 31:117-125

Ultrahigh Dimensional Feature Screening via RKHS Embeddings

Krishnakumar Balasubramanian, Bharath Sriperumbudur, Guy Lebanon ; PMLR 31:126-134

Meta-Transportability of Causal Effects: A Formal Approach

Elias Bareinboim, Judea Pearl ; PMLR 31:135-143

Convex Collective Matrix Factorization

Guillaume Bouchard, Dawei Yin, Shengbo Guo ; PMLR 31:144-152

Efficiently Sampling Probabilistic Programs via Program Analysis

Arun Chaganty, Aditya Nori, Sriram Rajamani ; PMLR 31:153-160

Computing the M Most Probable Modes of a Graphical Model

Chao Chen, Vladimir Kolmogorov, Yan Zhu, Dimitris Metaxas, Christoph Lampert ; PMLR 31:161-169

A simple criterion for controlling selection bias

Eunice Yuh-Jie Chen, Judea Pearl ; PMLR 31:170-177

Evidence Estimation for Bayesian Partially Observed MRFs

Yutian Chen, Max Welling ; PMLR 31:178-186

Why Steiner-tree type algorithms work for community detection

Mung Chiang, Henry Lam, Zhenming Liu, Vincent Poor ; PMLR 31:187-195

A simple sketching algorithm for entropy estimation over streaming data

Peter Clifford, Ioana Cosma ; PMLR 31:196-206

Deep Gaussian Processes

Andreas Damianou, Neil Lawrence ; PMLR 31:207-215

ODE parameter inference using adaptive gradient matching with Gaussian processes

Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone ; PMLR 31:216-228

Uncover Topic-Sensitive Information Diffusion Networks

Nan Du, Le Song, Hyenkyun Woo, Hongyuan Zha ; PMLR 31:229-237

Stochastic blockmodeling of relational event dynamics

Christopher DuBois, Carter Butts, Padhraic Smyth ; PMLR 31:238-246

Dynamic Copula Networks for Modeling Real-valued Time Series

Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan ; PMLR 31:247-255

Data-driven covariate selection for nonparametric estimation of causal effects

Doris Entner, Patrik Hoyer, Peter Spirtes ; PMLR 31:256-264

Learning to Top-K Search using Pairwise Comparisons

Brian Eriksson ; PMLR 31:265-273

Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension

Hamed Firouzi, Bala Rajaratnam, Alfred Hero III ; PMLR 31:274-288

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

Georg Goerg, Cosma Shalizi ; PMLR 31:289-297

Unsupervised Link Selection in Networks

Quanquan Gu, Charu Aggarwal, Jiawei Han ; PMLR 31:298-306

Clustered Support Vector Machines

Quanquan Gu, Jiawei Han ; PMLR 31:307-315

DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes

Abner Guzman-Rivera, Pushmeet Kohli, Dhruv Batra ; PMLR 31:316-324

Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers

Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba Vemuri ; PMLR 31:325-332

DYNACARE: Dynamic Cardiac Arrest Risk Estimation

Joyce Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh ; PMLR 31:333-341

Active Learning for Interactive Visualization

Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani ; PMLR 31:342-350

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions

Prabhanjan Kambadur, Aurelie Lozano ; PMLR 31:351-359

Beyond Sentiment: The Manifold of Human Emotions

Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa ; PMLR 31:360-369

Exact Learning of Bounded Tree-width Bayesian Networks

Janne Korhonen, Pekka Parviainen ; PMLR 31:370-378

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables

Nevena Lazic, Christopher Bishop, John Winn ; PMLR 31:379-387

Structure Learning of Mixed Graphical Models

Jason Lee, Trevor Hastie ; PMLR 31:388-396

Dynamic Scaled Sampling for Deterministic Constraints

Lei Li, Bharath Ramsundar, Stuart Russell ; PMLR 31:397-405

Learning Markov Networks With Arithmetic Circuits

Daniel Lowd, Amirmohammad Rooshenas ; PMLR 31:406-414

Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions

Heng Luo, Pierre Luc Carrier, Aaron Courville, Yoshua Bengio ; PMLR 31:415-423

Fast Near-GRID Gaussian Process Regression

Yuancheng Luo, Ramani Duraiswami ; PMLR 31:424-432

Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling

Jianzhu Ma, Jian Peng, Sheng Wang, Jinbo Xu ; PMLR 31:433-441

Thompson Sampling in Switching Environments with Bayesian Online Change Detection

Joseph Mellor, Jonathan Shapiro ; PMLR 31:442-450

A Last-Step Regression Algorithm for Non-Stationary Online Learning

Edward Moroshko, Koby Crammer ; PMLR 31:451-462

Competing with an Infinite Set of Models in Reinforcement Learning

Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner ; PMLR 31:463-471

Efficient Variational Inference for Gaussian Process Regression Networks

Trung Nguyen, Edwin Bonilla ; PMLR 31:472-480

High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers

Zheng Pan, Changshui Zhang ; PMLR 31:481-488

Bayesian Structure Learning for Functional Neuroimaging

Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell Poldrack, Jonathan Pillow ; PMLR 31:489-497

Random Projections for Support Vector Machines

Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas ; PMLR 31:498-506

Distribution-Free Distribution Regression

Barnabas Poczos, Aarti Singh, Alessandro Rinaldo, Larry Wasserman ; PMLR 31:507-515

Localization and Adaptation in Online Learning

Alexander Rakhlin, Ohad Shamir, Karthik Sridharan ; PMLR 31:516-526

A recursive estimate for the predictive likelihood in a topic model

James Scott, Jason Baldridge ; PMLR 31:527-535

Detecting Activations over Graphs using Spanning Tree Wavelet Bases

James Sharpnack, Aarti Singh, Akshay Krishnamurthy ; PMLR 31:536-544

Changepoint Detection over Graphs with the Spectral Scan Statistic

James Sharpnack, Aarti Singh, Alessandro Rinaldo ; PMLR 31:545-553

Central Limit Theorems for Conditional Markov Chains

Mathieu Sinn, Bei Chen ; PMLR 31:554-562

Statistical Tests for Contagion in Observational Social Network Studies

Greg Ver Steeg, Aram Galstyan ; PMLR 31:563-571

Completeness Results for Lifted Variable Elimination

Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel ; PMLR 31:572-580

Supervised Sequential Classification Under Budget Constraints

Kirill Trapeznikov, Venkatesh Saligrama ; PMLR 31:581-589

On the Asymptotic Optimality of Maximum Margin Bayesian Networks

Sebastian Tschiatschek, Franz Pernkopf ; PMLR 31:590-598

Collapsed Variational Bayesian Inference for Hidden Markov Models

Pengyu Wang, Phil Blunsom ; PMLR 31:599-607

Block Regularized Lasso for Multivariate Multi-Response Linear Regression

Weiguang Wang, Yingbin Liang, Eric Xing ; PMLR 31:608-617

Bethe Bounds and Approximating the Global Optimum

Adrian Weller, Tony Jebara ; PMLR 31:618-631

Dual Decomposition for Joint Discrete-Continuous Optimization

Christopher Zach ; PMLR 31:632-640

Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes

Ke Zhou, Hongyuan Zha, Le Song ; PMLR 31:641-649

Greedy Bilateral Sketch, Completion & Smoothing

Tianyi Zhou, Dacheng Tao ; PMLR 31:650-658

Scoring anomalies: a M-estimation formulation

Stéphan Clémençon, Jérémie Jakubowicz ; PMLR 31:659-667

subscribe via RSS

This site last compiled Mon, 29 May 2017 07:19:51 +0000
Github Account Copyright © PMLR 2017. All rights reserved.