Adaptive inference on general graphical models

Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu, Özgür Sümer; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:1-8

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Identifying reasoning patterns in games

Dimitrios Antos, Avi Pfeffer; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:9-18

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Learning inclusion-optimal chordal graphs

Vincent Auvray, Louis Wehenkel; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:18-25

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Clique matrices for statistical graph decomposition and parameterising restricted positive definite matrices

David Barber; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:26-33

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Sensitivity analysis in decision circuits

Debarun Bhattacharjya, Ross D. Shachter; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:34-42

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Greedy block coordinate descent for large scale Gaussian process regression

Liefeng Bo, Cristian Sminchisescu; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:43-52

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CORL: a continuous-state offset-dynamics reinforcement learner

Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:53-61

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On identifying total effects in the presence of latent variables and selection bias

Zhihong Cai, Manabu Kuroki; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:62-69

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Complexity of inference in graphical models

Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:70-78

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Approximating the partition function by deleting and then correcting for model edges

Arthur Choi, Adnan Darwiche; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:79-87

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Multi-view learning in the presence of view disagreement

C. Mario Christoudias, Raquel Urtasun, Trevor Darrell; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:88-96

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Bounds on the Bethe free energy for Gaussian networks

Botond Cseke, Tom Heskes; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:97-104

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Bayesian network learning by compiling to weighted MAX-SAT

James Cussens; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:105-112

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Identifying optimal sequential decisions

A. Philip Dawid, Vanessa Didelez; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:113-120

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Learning convex inference of marginals

Justin Domke; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:137-144

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Knowledge combination in graphical multiagent models

Quang Duong, Michael P. Wellman, Satinder Singh; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:145-152

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Projected subgradient methods for learning sparse Gaussians

John Duchi, Stephen Gould, Daphne Koller; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:153-160

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Almost optimal intervention sets for causal discovery

Frederick Eberhardt; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:161-168

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Gibbs sampling in factorized continuous-time Markov processes

Tal El-Hay, Nir Friedman, Raz Kupferman; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:169-178

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Convex point estimation using undirected Bayesian transfer hierarchies

Gal Elidan, Ben Packer, Geremy Heitz, Daphne Koller; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:179-187

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Learning and solving many-player games through a cluster-based representation

Sevan G. Ficici, David C. Parkes, Avi Pfeffer; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:188-195

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Constrained approximate maximum entropy learning of Markov random fields

Varun Ganapathi, David Vickrey, John Duchi, Daphne Koller; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:196-203

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Multi-view learning over structured and non-identical outputs

Kuzman Ganchev, João V. Graça, John Blitzer, Ben Taskar; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:204-211

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AND/OR importance sampling

Vibhav Gogate, Rina Dechter; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:212-219

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Church: a language for generative models

Noah D. Goodman, Vikash K. Mansinghka, Daniel Roy, Keith Bonawitz, Joshua B. Tenenbaum; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:220-229

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Latent topic models for hypertext

Amit Gruber, Michal Rosen-Zvi, Yair Weiss; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:230-239

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A game-theoretic analysis of updating sets of probabilities

Peter D. Grünwald, Joseph Y. Halpern; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:240-247

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Sampling first order logical particles

Hannaneh Hajishirzi, Eyal Amir; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:248-255

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Sparse stochastic finite-state controllers for POMDPs

Eric A. Hansen; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:256-263

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Convergent message-passing algorithms for inference over general graphs with convex free energies

Tamir Hazan, Amnon Shashua; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:264-273

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Learning when to take advice: a statistical test for achieving a correlated equilibrium

Greg Hines, Kate Larson; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:274-281

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Causal discovery of linear acyclic models with arbitrary distributions

Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:282-289

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Cumulative distribution networks and the derivative-sum-product algorithm

Jim C. Huang, Brendan J. Frey; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:290-297

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Toward experiential utility elicitation for interface customization

Bowen Hui, Craig Boutilier; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:298-305

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Speeding up planning in Markov decision processes via automatically constructed abstractions

Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:306-314

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Bayesian out-trees

Tony Jebara; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:315-324

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Feature selection via block-regularized regression

Seyoung Kim, Eric Xing; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:325-332

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The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification

Manabu Kuroki, Zhihong Cai; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:333-340

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Partitioned linear programming approximations for MDPs

Branislav Kveton, Milos Hauskrecht; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:341-348

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The computational complexity of sensitivity analysis and parameter tuning

Johan Kwisthout, Linda C. van der Gaag; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:349-356

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Small sample inference for generalization error in classification using the CUD bound

Eric B. Laber, Susan A. Murphy; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:357-365

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Discovering cyclic causal models by independent components analysis

Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:366-374

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Improving gradient estimation by incorporating sensor data

Gregory Lawrence, Stuart Russell; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:375-382

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Learning arithmetic circuits

Daniel Lowd, Pedro Domingos; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:383-392

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Estimation and clustering with infinite rankings

Marina Meilă, Le Bao; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:393-402

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The phylogenetic Indian Buffet process: a non-exchangeable nonparametric prior for latent features

Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:403-410

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Topic models conditioned on arbitrary features with Dirichlet-multinomial regression

David Mimno, Andrew McCallum; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:411-418

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Explanation trees for causal Bayesian networks

Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:427-434

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On the conditional independence implication problem: a lattice-theoretic approach

Mathias Niepert, Dirk Van Gucht, Marc Gyssens; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:435-443

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Learning hidden Markov models for regression using path aggregation

Keith Noto, Mark Craven; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:444-451

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Bounding search space size via (Hyper)tree decompositions

Lars Otten, Rina Dechter; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:452-459

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Observation subset selection as local compilation of performance profiles

Yan Radovilsky, Solomon Eyal Shimony; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:460-467

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Improving the accuracy and efficiency of MAP inference for Markov Logic

Sebastian Riedel; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:468-475

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Model-based Bayesian reinforcement learning in large structured domains

Stéphane Ross, Joelle Pineau; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:476-483

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CT-NOR: representing and reasoning about events in continuous time

Aleksandr Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, Richard Mortier; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:484-493

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Efficient inference in persistent Dynamic Bayesian Networks

Tomáš Šingliar, Denver H. Dash; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:494-502

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Tightening LP relaxations for MAP using message passing

David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:503-510

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Learning the Bayesian network structure: dirichlet prior versus data

Harald Steck; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:511-518

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New techniques for algorithm portfolio design

Matthew Streeter, Stephen F. Smith; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:519-527

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Dyna-style planning with linear function approximation and prioritized sweeping

Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael Bowling; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:528-536

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Flexible priors for exemplar-based clustering

Daniel Tarlow, Richard S. Zemel, Brendan J. Frey; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:537-545

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Propagation using Chain Event Graphs

Peter A. Thwaites, Jim Q. Smith, Robert G. Cowell; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:546-553

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Identifying dynamic sequential plans

Jin Tian; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:554-561

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Hierarchical POMDP controller optimization by likelihood maximization

Marc Toussaint, Laurent Charlin, Pascal Poupart; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:562-570

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Modelling local and global phenomena with sparse Gaussian processes

Jarno Vanhatalo, Aki Vehtari; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:571-578

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Continuous time dynamic topic models

Chong Wang, David Blei, David Heckerman; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:579-586

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Hybrid variational/gibbs collapsed inference in topic models

Max Welling, Yee Whye Teh, Bert Kappen; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:587-594

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Inference for multiplicative models

Ydo Wexler, Christopher Meek; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:595-602

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Refractor importance sampling

Haohai Yu, Robert A. van Engelen; Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:603-609

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