AI & Statistics 2013

Accepted Papers

Scoring anomalies: a M-estimation formulation
Stephan Clemencon, Telecom ParisTech; Jeremie Jakubowicz, Telecom Sud Management

Bayesian Estimation for Partially Observed MRFs
Yutian Chen, UC Irvine; Max Welling, University of California, Irvine

High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers
Zheng Pan, Tsinghua Univ.; Changshui Zhang, Tsinghua Univ.

Learning to Top-K Search using Pairwise Comparisons
Brian Eriksson, Technicolor

Stochastic blockmodeling of relational event dynamics
Christopher DuBois, UC Irvine; Carter Butts, UC Irvine; Padhraic Smyth, University of California Irvine

Unsupervised Link Selection in Networks
Quanquan Gu, CS, UIUC; Charu Aggarwal, IBM Research; Jiawei Han, UIUC

Beyond Sentiment: The Manifold of Human Emotions
Seungyeon Kim, Georgia Institute of Technolog; Fuxin Li, Georgia Institute of Technology; Guy Lebanon, Georgia Institute of Technology; Irfan Essa, Georgia Institute of Technology

Greedy Bilateral Sketch, Completion and Smoothing
Tianyi Zhou, Universityof Technology Sydney; Dacheng Tao, University of Technology, Sydney

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

Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes
Abner Guzman-Rivera, University of Illinois; Pushmeet Kohli, Microsoft Research Cambridge; Dhruv Batra, Virginia Tech

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Nevena Lazic, Microsoft Research; Christopher Bishop, ; John Winn,

DYNA-CARE: Dynamic Cardiac Arrest Risk Estimation
Joyce Ho, University of Texas at Austin; Yubin Park, University of Texas at Austin; Carlos Carvalho, University of Texas at Austin; Joydeep Ghosh, University of Texas at Austin

Competing with an Infinite Set of Models in Reinforcement Learning
Phuong Nguyen, Australian National University; Odalric-Ambrym Maillard, Montanuniversitaet Leoben; Daniil Ryabko, INRIA, Lille; Ronald Ortner, Montanuniversitaet Leoben

Central Limit Theorems for Conditional Markov Chains
Mathieu Sinn, IBM Research; Bei Chen, IBM Research - Ireland

Efficient Variational Inference for Gaussian Process Regression Networks
Trung Nguyen, ANU and NICTA; Edwin Bonilla, NICTA and ANU

Active Learning for Interactive Visualization
Tomoharu Iwata, University of Cambridge; Neil Houlsby, University of Cambridge; Zoubin Ghahramani, University of Cambridge

ODE parameter inference using adaptive gradient matching with Gaussian processes
Frank Dondelinger, Biomathematics and Statistics Scotland; Dirk Husmeier, University of Glasgow; Simon Rogers, University of Glasgow; Maurizio Filippone, University of Glasgow

Exact Learning of Bounded Tree-width Bayesian Networks
Janne Korhonen, University of Helsinki; Pekka Parviainen,

Completeness Results for Lifted Variable Elimination
Nima Taghipour, KU Leuven; Daan Fierens, KU LEUVEN; Guy Van den Broeck, UCLA; Jesse Davis, KU LEUVEN; Hendrik Blockeel, KU LEUVEN

Fast Near-GRID Gaussian Process Regression
Yuancheng Luo, University of Maryland; Ramani Duraiswami, University of Maryland

Convex Collective Matrix Factorization
Guillaume Bouchard, "Xerox Research Centre, Europe"; Dawei Yin, Lehigh University; Shengbo Guo, Xerox Research Centre Europe

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

Why Steiner-tree type algorithms work for community detection
Mung Chiang, Princeton University; Henry Lam, Boston University; Zhenming Liu, Princeton University; Harold Poor, Princeton University

Clustering Oligarchies
Margareta Ackerman, Caltech; Shai Ben David, ; David Loker, University of Waterloo; Sivan Sabato, Microsoft Research

Structure Learning of Mixed Graphical Models
Jason Lee, Computational Math & Engineeri; Trevor Hastie, Stanford University

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

Clustered Support Vector Machine
Quanquan Gu, CS, UIUC; Jiawei Han, UIUC

A Competitive Test for Uniformity of Monotone Distributions
Ashkan Jafarpour, Univ. of California, San Diego; Jayadev Acharya, University of California, San Diego; Alon Orlitsky, University of California, San Diego; Ananda Suresh, University of California, San Diego

Deep Gaussian Processes
Andreas Damianou, University of Sheffield; Neil Lawrence, University of Sheffield

Permutation estimation and minimax rates of identifiability
Olivier Collier, IMAGINE-ENPC / CREST-ENSAE; Arnak Dalalyan, Ecole des Ponts ParisTech

Bayesian Structure Learning for Functional Neuroimaging
Oluwasanmi Koyejo, University of Texas at Austin; Mijung Park, UT Austin; Russell Poldrack, University of Texas at Austin; Joydeep Ghosh, University of Texas at Austin; Jonathan Pillow, The University of Texas at Austin

Dual Decomposition for Joint Discrete-Continuous Optimization
Christopher Zach, Microsoft Research

Distribution-Free Distribution Regression
Barnabas Poczos, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry Wasserman,

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

Efficiently Sampling Probabilistic Programs via Program Analysis
Arun Chaganty, ; Aditya Nori, Microsoft Research India; Sriram Rajamani,

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

Ultrahigh Dimensional Feature Screening via RKHS Embeddings
Krishnakumar Balasubramanian, Gatech; Bharath Sriperumbudur, Cambridge University ; Guy Lebanon, Georgia Institute of Technology

Data-driven covariate selection for nonparametric estimation of causal effects
Doris Entner, University of Helsinki; Patrik Hoyer, ; Peter Spirtes,

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
Georg Goerg, Carnegie Mellon University; Cosma Shalizi, Carnegie Mellon University

Thompson Sampling in Switching Environments with Bayesian Online Change Detection
Joseph Mellor, University of Manchester; Jonathan Shapiro, University of Manchester

Collapsed Variational Bayesian Inference for Hidden Markov Models
Pengyu Wang, University of Oxford; Phil Blunsom, University of Oxford

Supervised Sequential Classification Under Budget Constraints
Kirill Trapeznikov, Boston University; Venkatesh Saligrama, Boston University; david Castanon, Boston University

Computing the M Most Probable Modes of a Graphical Model
Chao Chen, Rutgers University; Vladimir Kolmogorov, IST Austria; Yan Zhu, Rutgers University; Dimitris Metaxas, Rutgers University; Christoph Lampert, IST Austria

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

Random Projections for Support Vector Machines
Saurabh Paul, Rensselaer Polytechnic Inst; Christos Boutsidis, IBM; Malik Magdon-Ismail, ; Petros Drineas, RPI

A unifying representation for a class of dependent random measures
Nicholas Foti, Dartmouth College; Sinead Williamson, Carnegie Mellon University; Daniel Rockmore, Dartmouth College; Joseph Futoma, Dartmouth College

Dynamic Copula Networks for Modeling Real-valued Time Series
Elad Eban, Hebrew University; gideon Rothschild, Hebrew University; Adi Mizrahi, Hebrew University; Israel Nelken, Hebrew University; Gal Elidan, Hebrew University

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions
Prabhanjan Kambadur, IBM TJ Watson Research Center; Aurelie Lozano,

A recursive estimate for the predictive likelihood in a topic model
James Scott, ; Jason Baldridge, University of Texas at Austin

Nystrom Approximation for Large-Scale Determinantal Processes
Raja Hafiz Affandi, University of Pennsylvania; Emily Fox, ; Ben Taskar, University of Pennsylvania; Alex Kulesza,

A simple sketching algorithm for entropy estimation over streaming data
Ioana Cosma, University of Ottawa; Peter Clifford, University of Oxford

Detecting Activations over Graphs using Spanning Tree Wavelet Bases
James Sharpnack, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Akshay Krishnamurthy, CMU

Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes
Ke Zhou, Georgia institute of technolog; Le Song, Georgia institute of technology; Hongyuan Zha, Georgia institute of technology

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

Statistical Tests for Contagion in Observational Social Network Studies
Greg Ver Steeg, Information Sciences Institute; Aram Galstyan, Information Sciences Institute, USC

Diagonal Orthant Multinomial Probit Models
James Johndrow, Duke University; Kristian Lum, Virginia Tech; David Dunson, Duke University

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes
Andrej Aderhold, University of St Andrews; Dirk Husmeier, University of Glasgow; V. Anne Smith, University of St Andrews

Bayesian learning of joint distributions of objects
Anjishnu Banerjee, Duke University; Jared Murray, Duke University; David Dunson, Duke University

Consensus Ranking with Signed Permutations
Raman Arora, TTIC; Marina Meila, University of Washington

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series
Zhaoran Wang, Princeton University; Fang Han, Johns Hopkins University; Han Liu,

Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
Heng Luo, Universite de Montreal; Pierre Luc Carrier, Universite de Montreal; Aaron Courville, Universite de Montreal; Yoshua Bengio, Universite de Montreal

Block Regularized Lasso for Multivariate Multi-Response Linear Regression
Weiguang Wang, Syracuse University; Yingbin Liang, Syracuse University; Eric Xing, Carnegie Mellon University

Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
Hamed Firouzi, University of Michigan; Alfred Hero III, University of Michigan

Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Zhaoshi Meng, University of Michigan; Dennis Wei, University of Michigan; Ami Wiesel, The Hebrew University of Jerusalem ; Alfred Hero III, University of Michigan

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

Localization and Adaptation in Online Learning
Alexander (Sasha) Rakhlin, University of Pennsylvania; Ohad Shamir, ; Karthik Sridharan, University of Pennsylvania

Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers
Hesamoddin Salehian, University of Florida; Guang Cheng, ; Jeffrey Ho, UFL; Baba Vemuri, University of Florida

Distributed and Adaptive Darting Monte Carlo through Regenerations
Sungjin Ahn, UCI; Yutian Chen, UC Irvine; Max Welling, "University of California, Irvine"

Uncover Topic-Sensitive Information Diffusion Networks
NAN DU, GATECH; Le Song, Georgia institute of technology; Hyenkyun Woo, ; Hongyuan Zha, Georgia institute of technology

Learning Markov Networks With Arithmetic Circuits
Daniel Lowd, University of Oregon; Amirmohammad Rooshenas, University of Oregon

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