Learning and Inference in Tractable Probabilistic Knowledge Bases

Mathias Niepert, Pedro Domingos; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:

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The 31st Uncertainty in Artificial Intelligence Conference: Preface

Marina Meila, Tom Heskes; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:1-8

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Bethe and Related Pairwise Entropy Approximations

Adrian Weller; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:9-18

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Tracking with ranked signals

Tianyang Li UT Austin, Harsh Pareek UT Austin, Pradeep Ravikumar UT Austin, Dhruv Balwada Geophysical Fluid Dynamics Institute at Florida State University, Kevin Speer Geophysical Fluid Dynamics Institute at Florida State University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:19-28

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The Long-Run Behavior of Continuous Time Bayesian Networks

Liessman Sturlaugson Montana State University, John Sheppard Montana State University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:29-38

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Complexity of the Exact Solution to the Test Sequencing Problem

Wenhao Liu, Ross Shachter; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:39-48

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Budget Constraints in Prediction Markets

Nikhil Devanur, Miroslav Dudik, Zhiyi Huang, David Pennock; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:49-58

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Adversarial Cost-Sensitive Classification

Kaiser Asif U of Illinois at Chicago, Wei Xing U of Illinois at Chicago, Sima Behpour U of Illinois at Chicago, Brian Ziebart; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:59-68

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Intelligent Affect: Rational Decision Making for Socially Aligned Agents

Nabiha Asghar, Jesse Hoey; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:69-78

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Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models

Stefano Albrecht, Subramanian Ramamoorthy; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:79-88

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Finite-Sample Analysis of GTD Algorithms

Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:89-98

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Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features

Steven Cheng-Xian Li UMass Amherst, Benjamin Marlin UMass Amherst; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:99-108

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Extend Transferable Belief Models with Probabilistic Priors

Chunlai Zhou Renmin University of China, Yuan Feng; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:109-118

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Population Empirical Bayes

Alp Kucukelbir Columbia University, David Blei Columbia University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:119-128

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Computing Optimal Bayesian Decisions for Rank Aggregation via MCMC Sampling

David Hughes RPI, Kevin Hwang RPI, Lirong Xia Rensselaer Polytechnic Institute; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:129-138

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Incremental region selection for mini-bucket elimination bounds

Sholeh Forouzan, Alexander Ihler; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:139-148

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(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits

Nikita Mishra 1989, Abhradeep Thakurta Yahoo!; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:149-158

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Parameterizing the Distance Distribution of Undirected Networks

Christian Bauckhage, Kristian Kersting, Fabian Hadiji; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:159-168

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Planning under Uncertainty with Weighted State Scenarios

Erwin Walraven Delft University of Technology, Matthijs Spaan Delft University of Technology; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:169-178

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Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures

Saneem Ahmed CG, Harikrishna Narasimhan Indian Institute of Science, Shivani Agarwal Indian Institute of Science; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:179-188

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Annealed Gradient Descent for Deep Learning

Hengyue Pan York University, Hui Jiang York University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:189-198

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Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring

Konstantinos Georgatzis, Christopher Williams; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:199-208

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Progressive Abstraction Refinement for Sparse Sampling

Jesse Hostetler Oregon State University, Alan Fern Oregon State University, Thomas Dietterich Oregon State University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:209-218

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Learning the Structure of Sum-Product Networks via an SVD-based Algorithm

Tameem Adel Radboud University Nijmegen, David Balduzzi Victoria University of Wellington, Ali Ghodsi; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:219-228

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Learning the Structure of Causal Models with Relational and Temporal Dependence

Katerina Marazopoulou, Marc Maier, David Jensen; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:229-238

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On the Computability of AIXI

Jan Leike, Marcus Hutter; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:239-248

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Bethe Projections for Non-Local Inference

Luke Vilnis UMass Amherst, David Belanger UMass Amherst, Daniel Sheldon UMass Amherst, Andrew McCallum UMass Amherst; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:249-258

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Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

Anshumali Shrivastava Cornell University, Ping Li Rutgers University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:259-268

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A Finite Population Likelihood Ratio Test of the Sharp Null Hypothesis for Compliers

Wen Wei Loh, Thomas Richardson; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:269-278

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Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage

Marek Petrik, Xiaojian Wu UMASS; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:279-288

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Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization

Alnur Ali Carnegie Mellon University, J. Zico Kolter Carnegie Mellon University, Steven Diamond, Stephen Boyd; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:289-298

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Structure Learning Constrained by Node-Specific Degree Distribution

Jianzhu Ma TTIC, Qingming Tang TTIC, Sheng Wang TTIC, Feng Zhao TTIC, Jinbo Xu TTIC; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:299-307

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Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization

Sejun Park KAIST, Jinwoo Shin KAIST; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:308-317

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How matroids occur in the context of learning Bayesian network structure

Milan Studeny Inst. Info. Theory and Autom.; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:318-327

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Visual Causal Feature Learning

Krzysztof Chalupka Caltech, Pietro Perona Caltech, Frederick Eberhardt Caltech; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:328-337

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The Limits of Knowledge Compilation for Exact Model Counting

Vincent Liew, Paul Beame; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:338-347

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An Upper Bound on the Global Optimum in Parameter Estimation

Khaled Refaat UCLA, Adnan Darwiche UCLA; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:348-357

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Estimating the Partition Function by Discriminance Sampling

Qiang Liu, Jian Peng UIUC, Alexander Ihler, John Fisher III; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:358-366

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Fast Relative-Error Approximation Algorithm for Ridge Regression

Shouyuan Chen CUHK, Yang Liu, Michael Lyu Chinese University of Hong Kong, Irwin King, Shengyu Zhang; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:367-376

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Generalization Bounds for Transfer Learning under Model Shift

Xuezhi Wang Carnegie Mellon Univ., Jeff Schneider Carnegie Mellon Univ; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:377-386

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Do-calculus when the True Graph is Unknown

Antti Hyttinen, Frederick Eberhardt Caltech, Matti Järvisalo; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:387-396

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A Markov Game Model for Valuing Player Actions in Ice Hockey

Kurt Routley Simon Fraser University, Oliver Schulte Simon Fraser University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:397-406

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Impact of Learning Strategies on the Quality of Bayesian Networks: An Empirical Evaluation

Brandon Malone, Matti Järvisalo, Petri Myllymaki Helsinki Institute for Information Technology; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:407-416

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Clustered Sparse Bayesian Learning

Yu Wang, David Wipf Jeong Min Yun Wei Chen Ian Wassell; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:417-426

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Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

Guy Van den Broeck Karthika Mohan Arthur Choi UCLA, Judea Pearl; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:427-436

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Communication Efficient Coresets for Empirical Loss Minimization

Sashank Jakkam Reddi Carnegie Mellon University, Barnabas Poczos Alex Smola; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:437-446

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Importance sampling over sets: a new probabilistic inference scheme

Stefan Hadjis, Stefano Ermon; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:447-456

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Novel Bernstein-like Concentration Inequalities for the Missing Mass

Bahman Yari Saeed Khanloo Monash, Gholamreza Haffari Monash University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:457-466

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A Complete Generalized Adjustment Criterion

Emilija Perkovic, Johannes Textor Utrecht University, Markus Kalisch, Marloes Maathuis; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:467-476

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Locally Conditioned Belief Propagation

Thomas Geier Ulm University, Felix Richter Ulm University, Susanne Biundo Ulm University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:477-486

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Optimal expert elicitation to reduce interval uncertainty

Nadia Ben Abdallah, Sébastien Destercke; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:487-496

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Off-policy learning based on weighted importance sampling with linear computational complexity

Ashique Rupam Mahmood, Richard Sutton; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:497-506

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State Sequence Analysis in Hidden Markov Models

Yuri Grinberg Ottawa Hospital Research Inst., Theodore Perkins Ottawa Hospital Research Institute; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:507-515

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On the Error of Random Fourier Features

Danica Sutherland Carnegie Mellon University, Jeff Schneider Carnegie Mellon Univ; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:516-525

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A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC

Wei WANG UPMC, Stuart Russell; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:526-535

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Encoding Markov logic networks in Possibilistic Logic

Ondrej Kuzelka Cardiff University, Jesse Davis KU Leuven, Steven Schockaert Cardiff University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:536-545

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Approximate Probabilistic Inference in Hybrid Domains by Hashing

Vaishak Belle KU Leuven, Guy Van den Broeck KU Leuven, Andrea Passerini; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:546-555

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Bayesian Network Learning with Discrete Case-Control Data

Giorgos Borboudakis, Ioannis Tsamardinos; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:556-565

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Learning Optimal Chain Graphs with Answer Set Programming

Dag Sonntag Linköping University, Matti Järvisalo, Jose Pena Linkoping University, Antti Hyttinen; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:566-575

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Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints

Yun Zhou Queen Mary University of Londo, Norman Fenton Queen Mary University of London, Timothy Hospedales Queen Mary University of London, Martin Neil Queen Mary University of London; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:576-585

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Large-scale randomized-coordinate descent methods with non-separable linear constraints

Ahmed Hefny Carnegie Mellon University, Sashank Jakkam Reddi Carnegie Mellon University, Carlton Downey Carnegie Mellon University, Avinava Dubey Carnegie Mellon University, Suvrit Sra; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:586-595

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Stable Spectral Learning Based on Schur Decomposition

Nikos Vlassis Adobe, Nicolo Colombo LCSB Univ of Luxembourg; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:596-603

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Budgeted Online Collective Inference

Jay Pujara, Ben London, Lise Getoor; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:604-613

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Missing Data as a Causal and Probabilistic Problem

Ilya Shpitser, Karthika Mohan UCLA, Judea Pearl UCLA; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:614-623

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Scalable Recommendation with Hierarchical Poisson Factorization

Prem Gopalan Princeton University, Jake Hofman, David Blei Columbia University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:624-633

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Large-Margin Determinantal Point Processes

Boqing Gong, Wei-Lun Chao USC, Kristen Grauman U. of Texas at Austin, Fei Sha USC; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:634-643

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Psychophysical Testing with Bayesian Active Learning

Jacob Gardner Washington University in St. L, Xinyu Song Washington University in St. Louis, Kilian Weinberger Washington University in St. Louis, John Cunningham Columbia University, Dennis Barbour Washington University in St. Louis; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:644-653

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Learning from Pairwise Marginal Independencies

Johannes Textor Utrecht University, Alexander Idelberger, Maciej Liskiewicz; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:654-663

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Estimating Mutual Information by Local Gaussian Approximation

Shuyang Gao USC, Greg Ver Steeg Information Sciences Institute, Aram Galstyan Information Sciences Institute; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:664-671

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Semi-described and semi-supervised learning with Gaussian processes

Andreas Damianou, Neil Lawrence; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:672-681

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Bayesian Structure Learning for Stationary Time Series

Alex Tank, Emily Fox, Nicholas Foti; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:682-691

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Equitable Partitions of Concave Free Energies

Martin Mladenov, Kristian Kersting; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:692-701

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Learning to generate via MMD optimization

Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel Roy; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:702-711

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Non-parametric Revenue Optimization for Generalized Second Price auctions.

Mehryar Mohri NYU, Andres Munoz Medina NYU; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:712-721

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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:722-731

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Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Jason Xu, Vladimir Minin; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:732-741

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Survival Filter: A Latent Timeseries Model for Joint Survival Analysis

Rajesh Ranganath Princeton University, Adler Perotte Columbia University, Noemie Elhadad Columbia University, David Blei Columbia University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:742-751

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High-Dimensional Stochastic Integration via Error-Correcting Codes

Dimitris Achlioptas UCSC, Pei Jiang; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:752-761

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Geometric Network Comparisons

Dena Asta Carnegie Mellon University, Cosma Shalizi Carnegie Mellon University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:762-770

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Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations

Max Chickering, Chris Meek; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:771-779

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A Probabilistic Logic for Reasoning about Uncertain Temporal Information

Dragan Doder, Zoran Ognjanovic Mathematical Institute Serbian Academy of Sciences and Arts; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:780-789

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Hamiltonian ABC

Edward Meeds, Max Welling Robert Leenders; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:790-799

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Memory-Efficient Symbolic Online Planning for Factored MDPs

Aswin Raghavan Oregon State University, Prasad Tadepalli Oregon State University, Alan Fern Oregon State University, Roni Khardon Tufts University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:800-809

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Bayesian Optimal Control of Smoothly Parameterized Systems

Yasin Abbasi-Yadkori QUT, Csaba Szepesvari; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:810-819

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Learning and Planning with Timing Information in Markov Decision Processes

Pierre-Luc Bacon McGill University, Borja Balle McGill University, Doina Precup McGill University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:820-829

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Online Bellman Residual Algorithms with Predictive Error Guarantees

Wen Sun Carnegie Mellon University, J. Andrew Bagnell Carnegie Mellon University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:830-839

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Fast Algorithms for Learning with Long $N$-grams via Suffix Tree Based Matrix Multiplication

Hristo Paskov, Trevor Hastie, John Mitchell; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:840-849

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Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information

Herve Isambert CNRS, Severine Affeldt Institut Curie; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:850-859

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Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models

Zhen Qin, Christian Shelton; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:860-869

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Averaging of Decomposable Graphs by Dynamic Programming and Sampling

Kustaa Kangas, Teppo Niinimäki, Mikko Koivisto Helsinki Institute for Information Technology; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:870-879

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Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar

Ardavan Salehi Nobandegani McGill University, Ioannis Psaromiligkos McGill University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:880-889

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Mesochronal Structure Learning

Sergey Plis, Jianyu Yang, David Danks Carnegie Mellon University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:890-899

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A Statistical Framework for Clustering Representation Learning

Hassan Ashtiani, Shai Ben-David; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:900-909

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Active Search on Graphs using Sigma-Optimality

Yifei Ma, Tzu-Kuo Huang, Jeff Schneider Carnegie Mellon Univ; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:910-919

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Multitasking: Optimal Planning for Bandit Superprocesses

Dylan Hadfield-Menell, Stuart Russell; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:920-929

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Revisiting Non-Progressive Influence Models: Scalable Influence Maximization in Social Networks

Golshan Golnari, Amir Asiaee T., Arindam Banerjee, Zhi-Li Zhang; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:930-939

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Learning Latent Variable Models via Method of Moments and Exterior Point Optimization

Amirreza Shaban Georgia Institute of Technolog, Mehrdad Farajtabar Georgia Institute of Technology, Bo Xie Georgia Institute of Technology, Le Song Georgia Tech, Byron Boots Georgia Institute of Technology; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:940-949

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Zero-Truncated Poisson Model for Scalable Bayesian Factorization of Massive Binary Tensors with Mode-Networks

Changwei Hu Duke University, Piyush Rai Duke University, Lawrence Carin Duke University; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:950-959

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Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts

Adrian Kim Seoul National University, Kyomin Jung Daniel Tarlow Pushmeet Kohli; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:960-968

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Polynomial-time algorithm for learning optimal tree-augmented dynamic Bayesian networks

Alexandra Carvalho Instituto de Telecomunicações, José Monteiro IST, Susana Vinga IDMEC; Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:969-978

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