UAI 2013

84 papers

A Lightweight Inference Method for Image Classification John Mark Agosta, Preeti J. Pillai

A Sound and Complete Algorithm for Learning Causal Models from Relational Data Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen

Active Learning with Expert Advice Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang

Active Sensing as Bayes-Optimal Sequential Decision Making Sheeraz Ahmad, Angela J. Yu

Advances in Bayesian Network Learning Using Integer Programming James Cussens, Mark Bartlett

An Object-Oriented Spatial and Temporal Bayesian Network for Managing Willows in an American Heritage River Catchment Lauchlin A. T. Wilkinson, Yung En Chee, Ann E. Nicholson, Pedro Quintana-Ascencio

Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs Charles Tripp, Ross D. Shachter

Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes Patrice Perny, Paul Weng, Judy Goldsmith, Josiah Hanna

Automorphism Groups of Graphical Models and Lifted Variational Inference Hung Bui, Tuyen N. Huynh, Sebastian Riedel

Batch-iFDD for Representation Expansion in Large MDPs Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan P. How

Bayesian Supervised Dictionary Learning‎ Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Mohammadreza Zolfaghari, Mohammad T. Manzuri Shalmani

Bennett-Type Generalization Bounds: Large-Deviation Case and Faster Rate of Convergence Chao Zhang

Bethe-ADMM for Tree Decomposition Based Parallel MAP Inference Qiang Fu, Huahua Wang, Arindam Banerjee

Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function Nicholas Ruozzi

Boosting in the Presence of Label Noise Jakramate Bootkrajang, Ata Kabán

Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs Luis Gustavo Vianna, Scott Sanner, Leliane Nunes de Barros

Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens Ravi Ganti, Alexander G. Gray

Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models Peter Spirtes

Causal Transportability of Experiments on Controllable Subsets of Variables: Z-Transportability Sanghack Lee, Vasant G. Honavar

Collective Diffusion over Networks: Models and Inference Akshat Kumar, Daniel Sheldon, Biplav Srivastava

Constrained Bayesian Inference for Low Rank Multitask Learning Oluwasanmi Koyejo, Joydeep Ghosh

Convex Relaxations of Bregman Divergence Clustering Hao Cheng, Xinhua Zhang, Dale Schuurmans

Cyclic Causal Discovery from Continuous Equilibrium Data Joris M. Mooij, Tom Heskes

Debugging the Evidence Chain Russell G. Almond, Yoon Jeon Kim, Valerie J. Shute, Matthew Ventura

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering Amar Shah, Zoubin Ghahramani

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo

Dynamic Blocking and Collapsing for Gibbs Sampling Deepak Venugopal, Vibhav Gogate

Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks Brandon M. Malone, Changhe Yuan

Evaluating Computational Models of Explanation Using Human Judgments Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths

Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing Lu Zheng, Ole J. Mengshoel

Finite-Time Analysis of Kernelised Contextual Bandits Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini

From Ordinary Differential Equations to Structural Causal Models: The Deterministic Case Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

Gaussian Processes for Big Data James Hensman, Nicoló Fusi, Neil D. Lawrence

Generative Multiple-Instance Learning Models for Quantitative Electromyography Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte

High-Dimensional Joint Sparsity Random Effects Model for Multi-Task Learning Krishnakumar Balasubramanian, Kai Yu, Tong Zhang

Hilbert Space Embeddings of Predictive State Representations Byron Boots, Geoffrey J. Gordon, Arthur Gretton

Hinge-Loss Markov Random Fields: Convex Inference for Structured Prediction Stephen H. Bach, Bert Huang, Ben London, Lise Getoor

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

Identifying Learning Trajectories in an Educational Video Game Deirdre Kerr, Gregory K. W. K. Chung

Integrating Document Clustering and Topic Modeling Pengtao Xie, Eric P. Xing

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models Jean Honorio, Tommi S. Jaakkola

Latent Topic Analysis for Predicting Group Purchasing Behavior on the Social Web Feng-Tso Sun, Yi-Ting Yeh, Ole J. Mengshoel, Martin L. Griss

Learning Max-Margin Tree Predictors Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson

Learning Parameters by Prediction Markets and Kelly Rule for Graphical Models Wei Sun, Robin Hanson, Kathryn B. Laskey, Charles Twardy

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries James McInerney, Alex Rogers, Nicholas R. Jennings

Learning Sparse Causal Models Is Not NP-Hard Tom Claassen, Joris M. Mooij, Tom Heskes

Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu

Modeling Documents with Deep Boltzmann Machines Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton

Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency Zohar Feldman, Carmel Domshlak

Multiple Instance Learning by Discriminative Training of Markov Networks Hossein Hajimirsadeghi, Jinling Li, Greg Mori, Mohamed H. Zaki, Tarek Sayed

Normalized Online Learning Stéphane Ross, Paul Mineiro, John Langford

On MAP Inference by MWSS on Perfect Graphs Adrian Weller, Tony Jebara

On the Complexity of Strong and Epistemic Credal Networks Denis Deratani Mauá, Cassio Polpo de Campos, Alessio Benavoli, Alessandro Antonucci

One-Class Support Measure Machines for Group Anomaly Detection Krikamol Muandet, Bernhard Schölkopf

Optimization with Parity Constraints: From Binary Codes to Discrete Integration Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, Patrick Jaillet

Pay or Play Sigal Oren, Michael Schapira, Moshe Tennenholtz

POMDPs Under Probabilistic Semantics Krishnendu Chatterjee, Martin Chmelik

Predicting Latent Variables with Knowledge and Data: A Case Study in Trauma Care Barbaros Yet, William Marsh, Zane Perkins, Nigel Tai, Norman E. Fenton

Preference Elicitation for General Random Utility Models Hossein Azari Soufiani, David C. Parkes, Lirong Xia

Probabilistic Conditional Preference Networks Damien Bigot, Bruno Zanuttini, Hélène Fargier, Jérôme Mengin

Probabilistic Inverse Reinforcement Learning in Unknown Environments Aristide C. Y. Tossou, Christos Dimitrakakis

Product Trees for Gaussian Process Covariance in Sublinear Time David A. Moore, Stuart Russell

Qualitative Possibilistic Mixed-Observable MDPs Nicolas Drougard, Florent Teichteil-Königsbuch, Jean-Loup Farges, Didier Dubois

Reasoning About Probabilities in Dynamic Systems Using Goal Regression Vaishak Belle, Hector J. Levesque

Sample Complexity of Multi-Task Reinforcement Learning Emma Brunskill, Lihong Li

Scalable Matrix-Valued Kernel Learning for High-Dimensional Nonlinear Multivariate Regression and Granger Causality Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano

Scoring and Searching over Bayesian Networks with Causal and Associative Priors Giorgos Borboudakis, Ioannis Tsamardinos

Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation Marek Petrik, Dharmashankar Subramanian, Janusz Marecki

Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search Arindam Khaled, Eric A. Hansen, Changhe Yuan

Sparse Nested Markov Models with Log-Linear Parameters Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins

SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure Eliot Brenner, David A. Sontag

Speedy Model Selection (SMS) for Copula Models Yaniv Tenzer, Gal Elidan

Stochastic Rank Aggregation Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng

Structured Convex Optimization Under Submodular Constraints Kiyohito Nagano, Yoshinobu Kawahara

Structured Message Passing Vibhav Gogate, Pedro M. Domingos

The Bregman Variational Dual-Tree Framework Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht

The Lovasz-Bregman Divergence and Connections to Rank Aggregation, Clustering, and Web Ranking Rishabh K. Iyer, Jeff A. Bilmes

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani

Tighter Linear Program Relaxations for High Order Graphical Models Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss

Transforming Personal Artifacts into Probabilistic Narratives Setareh Rafatirad, Kathryn B. Laskey

Treedy: A Heuristic for Counting and Sampling Subsets Teppo Niinimaki, Mikko Koivisto

Unsupervised Learning of Noisy-or Bayesian Networks Yonatan Halpern, David A. Sontag

Warped Mixtures for Nonparametric Cluster Shapes Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani