AISTATS 2013

71 papers

Scoring anomalies: a M-estimation formulation

Stéphan Clémençon, Jérémie Jakubowicz

Greedy Bilateral Sketch, Completion & Smoothing

Tianyi Zhou, Dacheng Tao

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

Ke Zhou, Hongyuan Zha, Le Song

Dual Decomposition for Joint Discrete-Continuous Optimization

Christopher Zach

Bethe Bounds and Approximating the Global Optimum

Adrian Weller, Tony Jebara

Block Regularized Lasso for Multivariate Multi-Response Linear Regression

Weiguang Wang, Yingbin Liang, Eric Xing

Collapsed Variational Bayesian Inference for Hidden Markov Models

Pengyu Wang, Phil Blunsom

On the Asymptotic Optimality of Maximum Margin Bayesian Networks

Sebastian Tschiatschek, Franz Pernkopf

Supervised Sequential Classification Under Budget Constraints

Kirill Trapeznikov, Venkatesh Saligrama

Completeness Results for Lifted Variable Elimination

Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel

Statistical Tests for Contagion in Observational Social Network Studies

Greg Ver Steeg, Aram Galstyan

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

Detecting Activations over Graphs using Spanning Tree Wavelet Bases

James Sharpnack, Aarti Singh, Akshay Krishnamurthy

A recursive estimate for the predictive likelihood in a topic model

James Scott, Jason Baldridge

Localization and Adaptation in Online Learning

Alexander Rakhlin, Ohad Shamir, Karthik Sridharan

Distribution-Free Distribution Regression

Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry Wasserman

Random Projections for Support Vector Machines

Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas

Bayesian Structure Learning for Functional Neuroimaging

Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell Poldrack, Jonathan Pillow

High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers

Zheng Pan, Changshui Zhang

Efficient Variational Inference for Gaussian Process Regression Networks

Trung Nguyen, Edwin Bonilla

Competing with an Infinite Set of Models in Reinforcement Learning

Phuong Nguyen, Odalric-ambrym Maillard, Daniil Ryabko, Ronald Ortner

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

Edward Moroshko, Koby Crammer

Thompson Sampling in Switching Environments with Bayesian Online Change Detection

Joseph Mellor, Jonathan Shapiro

Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling

Jianzhu Ma, Jian Peng, Sheng wang, Jinbo Xu

Fast Near-GRID Gaussian Process Regression

Yuancheng Luo, Ramani Duraiswami

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

Heng Luo, Pierre Luc Carrier, Aaron Courville, Yoshua Bengio

Learning Markov Networks With Arithmetic Circuits

Daniel Lowd, Amirmohammad Rooshenas

Dynamic Scaled Sampling for Deterministic Constraints

Lei Li, Bharath Ramsundar, Stuart Russell

Structure Learning of Mixed Graphical Models

Jason Lee, Trevor Hastie

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

Nevena Lazic, Christopher Bishop, John Winn

Exact Learning of Bounded Tree-width Bayesian Networks

Janne Korhonen, Pekka Parviainen

Beyond Sentiment: The Manifold of Human Emotions

Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa

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

Prabhanjan Kambadur, Aurélie Lozano

Active Learning for Interactive Visualization

Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani

DYNACARE: Dynamic Cardiac Arrest Risk Estimation

Joyce Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh

Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers

Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba Vemuri

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

Abner Guzmán-rivera, Pushmeet Kohli, Dhruv Batra

Clustered Support Vector Machines

Quanquan Gu, Jiawei Han

Unsupervised Link Selection in Networks

Quanquan Gu, Charu Aggarwal, Jiawei Han

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

Georg Goerg, Cosma Shalizi

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

Hamed Firouzi, Bala Rajaratnam, Alfred Hero III

Learning to Top-K Search using Pairwise Comparisons

Brian Eriksson

Data-driven covariate selection for nonparametric estimation of causal effects

Doris Entner, Patrik Hoyer, Peter Spirtes

Dynamic Copula Networks for Modeling Real-valued Time Series

Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan

Stochastic blockmodeling of relational event dynamics

Christopher DuBois, Carter Butts, Padhraic Smyth

Uncover Topic-Sensitive Information Diffusion Networks

Nan Du, Le Song, Hyenkyun Woo, Hongyuan Zha

ODE parameter inference using adaptive gradient matching with Gaussian processes

Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone

Deep Gaussian Processes

Andreas Damianou, Neil D. Lawrence

A simple sketching algorithm for entropy estimation over streaming data

Peter Clifford, Ioana Cosma

Why Steiner-tree type algorithms work for community detection

Mung Chiang, Henry Lam, Zhenming Liu, Vincent Poor

Evidence Estimation for Bayesian Partially Observed MRFs

Yutian Chen, Max Welling

A simple criterion for controlling selection bias

Eunice Yuh-Jie Chen, Judea Pearl

Computing the M Most Probable Modes of a Graphical Model

Chao Chen, Vladimir Kolmogorov, Yan Zhu, Dimitris Metaxas, Christoph Lampert

Efficiently Sampling Probabilistic Programs via Program Analysis

Arun Chaganty, Aditya Nori, Sriram Rajamani

Convex Collective Matrix Factorization

Guillaume Bouchard, Dawei Yin, Shengbo Guo

Meta-Transportability of Causal Effects: A Formal Approach

Elias Bareinboim, Judea Pearl

Ultrahigh Dimensional Feature Screening via RKHS Embeddings

Krishnakumar Balasubramanian, Bharath Sriperumbudur, Guy Lebanon

Consensus Ranking with Signed Permutations

Raman Arora, Marina Meila

Distributed and Adaptive Darting Monte Carlo through Regenerations

Sungjin Ahn, Yutian Chen, Max Welling

Further Optimal Regret Bounds for Thompson Sampling

Shipra Agrawal, Navin Goyal

Nyström Approximation for Large-Scale Determinantal Processes

Raja Hafiz Affandi, Alex Kulesza, Emily Fox, Ben Taskar

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Andrej Aderhold, Dirk Husmeier, V. Anne Smith

Clustering Oligarchies

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

A Competitive Test for Uniformity of Monotone Distributions

Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Suresh

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series

Zhaoran Wang, Fang Han, Han Liu

Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods

Zhaoshi Meng, Dennis Wei, Ami Wiesel, Alfred Hero III

Diagonal Orthant Multinomial Probit Models

James Johndrow, David Dunson, Kristian Lum

A unifying representation for a class of dependent random measures

Nicholas Foti, Joseph Futoma, Daniel Rockmore, Sinead Williamson

Permutation estimation and minimax rates of identifiability

Olivier Collier, Arnak Dalalyan

Bayesian learning of joint distributions of objects

Anjishnu Banerjee, Jared Murray, David Dunson