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ORCID31st UAI 2015: Amsterdam, The Netherlands
- Marina Meila, Tom Heskes:
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands. AUAI Press 2015, ISBN 978-0-9966431-0-8 - Yasin Abbasi-Yadkori, Csaba Szepesvári:
Bayesian Optimal Control of Smoothly Parameterized Systems. 1-11 - Nadia Ben Abdallah, Sébastien Destercke:
Optimal expert elicitation to reduce interval uncertainty. 12-21 - Dimitris Achlioptas, Pei Jiang:
Stochastic Integration via Error-Correcting Codes. 22-31 - Tameem Adel, David Balduzzi, Ali Ghodsi:
Learning the Structure of Sum-Product Networks via an SVD-based Algorithm. 32-41 - Séverine Affeldt, Hervé Isambert:
Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information. 42-51 - Stefano V. Albrecht, Subramanian Ramamoorthy:
Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models. 52-61 - Alnur Ali, J. Zico Kolter, Steven Diamond, Stephen P. Boyd:
Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization. 62-71 - Nabiha Asghar, Jesse Hoey:
Intelligent Affect: Rational Decision Making for Socially Aligned Agents. 72-81 - Hassan Ashtiani, Shai Ben-David:
Representation Learning for Clustering: A Statistical Framework. 82-91 - Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart:
Adversarial Cost-Sensitive Classification. 92-101 - Dena Marie Asta, Cosma Rohilla Shalizi:
Geometric Network Comparisons. 102-110 - Pierre-Luc Bacon, Borja Balle, Doina Precup:
Learning and Planning with Timing Information in Markov Decision Processes. 111-120 - Christian Bauckhage, Kristian Kersting, Fabian Hadiji:
Parameterizing the Distance Distribution of Undirected Networks. 121-130 - Paul Beame, Vincent Liew:
New Limits for Knowledge Compilation and Applications to Exact Model Counting. 131-140 - Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. 141-150 - Giorgos Borboudakis, Ioannis Tsamardinos:
Bayesian Network Learning with Discrete Case-Control Data. 151-160 - Guy Van den Broeck, Karthika Mohan, Arthur Choi, Adnan Darwiche, Judea Pearl:
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. 161-170 - C. G. Saneem Ahmed, Harikrishna Narasimhan, Shivani Agarwal:
Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures. 171-180 - Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt:
Visual Causal Feature Learning. 181-190 - Wei-Lun Chao, Boqing Gong, Kristen Grauman, Fei Sha:
Large-Margin Determinantal Point Processes. 191-200 - Shouyuan Chen, Yang Liu, Michael R. Lyu, Irwin King, Shengyu Zhang:
Fast Relative-Error Approximation Algorithm for Ridge Regression. 201-210 - David Maxwell Chickering, Christopher Meek:
Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations. 211-219 - Nicolò Colombo, Nikos Vlassis:
Stable Spectral Learning Based on Schur Decomposition. 220-227 - Andreas C. Damianou, Neil D. Lawrence:
Semi-described and semi-supervised learning with Gaussian processes. 228-237 - Nikhil R. Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock:
Budget Constraints in Prediction Markets. 238-247 - Dragan Doder, Zoran Ognjanovic:
A Probabilistic Logic for Reasoning about Uncertain Temporal Information. 248-257 - Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani:
Training generative neural networks via Maximum Mean Discrepancy optimization. 258-267 - Sholeh Forouzan, Alexander Ihler:
Incremental Region Selection for Mini-bucket Elimination Bounds. 268-277 - Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Estimating Mutual Information by Local Gaussian Approximation. 278-285 - Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham:
Psychophysical Detection Testing with Bayesian Active Learning. 286-295 - Thomas Geier, Felix Richter, Susanne Biundo:
Locally Conditioned Belief Propagation. 296-305 - Konstantinos Georgatzis, Christopher K. I. Williams:
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. 306-315 - Golshan Golnari, Amir Asiaee T., Arindam Banerjee, Zhi-Li Zhang:
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization in Social Networks. 316-325 - Prem Gopalan, Jake M. Hofman, David M. Blei:
Scalable Recommendation with Hierarchical Poisson Factorization. 326-335 - Yuri Grinberg, Theodore J. Perkins:
State Sequence Analysis in Hidden Markov Models. 336-344 - Dylan Hadfield-Menell, Stuart Russell:
Multitasking: Optimal Planning for Bandit Superprocesses. 345-354 - Stefan Hadjis, Stefano Ermon:
Importance Sampling over Sets: A New Probabilistic Inference Scheme. 355-364 - Jesse Hostetler, Alan Fern, Thomas G. Dietterich:
Progressive Abstraction Refinement for Sparse Sampling. 365-374 - Changwei Hu, Piyush Rai, Lawrence Carin:
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors. 375-384 - David Hughes, Kevin Hwang, Lirong Xia:
Computing Optimal Bayesian Decisions for Rank Aggregation via MCMC Sampling. 385-394 - Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Do-calculus when the True Graph Is Unknown. 395-404 - Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. 405-414 - Kustaa Kangas, Teppo Mikael Niinimäki, Mikko Koivisto:
Averaging of Decomposable Graphs by Dynamic Programming and Sampling. 415-424 - Bahman Yari Saeed Khanloo, Gholamreza Haffari:
Novel Bernstein-like Concentration Inequalities for the Missing Mass. 425-434 - Adrian Kim, Kyomin Jung, Yongsub Lim, Daniel Tarlow, Pushmeet Kohli:
Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts. 435-443 - Alp Kucukelbir, David M. Blei:
Population Empirical Bayes. 444-453 - Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Encoding Markov logic networks in Possibilistic Logic. 454-463 - Jan Leike, Marcus Hutter:
On the Computability of AIXI. 464-473 - Tianyang Li, Harsh H. Pareek, Pradeep Ravikumar, Dhruv Balwada, Kevin Speer:
Tracking with ranked signals. 474-483 - Steven Cheng-Xian Li, Benjamin M. Marlin:
Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features. 484-493 - Wenhao Liu, Ross D. Shachter:
Complexity of the Exact Solution to the Test Sequencing Problem. 494-503 - Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of Proximal Gradient TD Algorithms. 504-513 - Qiang Liu, Jian Peng, Alexander Ihler, John W. Fisher III:
Estimating the Partition Function by Discriminance Sampling. 514-522 - Wen Wei Loh
, Thomas S. Richardson:
A Finite Population Likelihood Ratio Test of the Sharp Null Hypothesis for Compliers. 523-532 - Jianzhu Ma, Feng Zhao, Jinbo Xu:
Structure Learning Constrained by Node-Specific Degree Distribution. 533-541 - Yifei Ma, Tzu-Kuo Huang, Jeff G. Schneider:
Active Search and Bandits on Graphs using Sigma-Optimality. 542-551 - Ashique Rupam Mahmood, Richard S. Sutton:
Off-policy learning based on weighted importance sampling with linear computational complexity. 552-561 - Brandon M. Malone, Matti Järvisalo, Petri Myllymäki:
Impact of Learning Strategies on the Quality of Bayesian Networks: An Empirical Evaluation. 562-571 - Katerina Marazopoulou, Marc E. Maier, David D. Jensen:
Learning the Structure of Causal Models with Relational and Temporal Dependence. 572-581 - Edward Meeds, Robert Leenders, Max Welling:
Hamiltonian ABC. 582-591 - Nikita Mishra, Abhradeep Thakurta:
(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits. 592-601 - Martin Mladenov, Kristian Kersting:
Equitable Partitions of Concave Free Energies. 602-611 - Mehryar Mohri, Andres Muñoz Medina:
Non-parametric Revenue Optimization for Generalized Second Price auctions.. 612-621 - José L. Monteiro, Susana Vinga, Alexandra M. Carvalho:
Polynomial-time algorithm for learning optimal tree-augmented dynamic Bayesian networks. 622-631 - Mathias Niepert, Pedro M. Domingos:
Learning and Inference in Tractable Probabilistic Knowledge Bases. 632-641 - Ardavan Salehi Nobandegani, Ioannis N. Psaromiligkos:
Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar. 642-651 - Hengyue Pan, Hui Jiang:
Annealed Gradient Descent for Deep Learning. 652-661 - Sejun Park, Jinwoo Shin:
Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization. 662-671 - Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
Fast Algorithms for Learning with Long N-grams via Suffix Tree Based Matrix Multiplication. 672-681 - Emilija Perkovic, Johannes Textor, Markus Kalisch, Marloes H. Maathuis:
A Complete Generalized Adjustment Criterion. 682-691 - Marek Petrik, Xiaojian Wu:
Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage. 692-701 - Sergey M. Plis, David Danks, Jianyu Yang:
Mesochronal Structure Learning. 702-711 - Jay Pujara, Ben London, Lise Getoor:
Budgeted Online Collective Inference. 712-721 - Zhen Qin, Christian R. Shelton:
Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models. 722-731 - Aswin Raghavan, Roni Khardon, Prasad Tadepalli, Alan Fern:
Memory-Effcient Symbolic Online Planning for Factored MDPs. 732-741 - Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
The Survival Filter: Joint Survival Analysis with a Latent Time Series. 742-751 - Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola:
Communication Efficient Coresets for Empirical Loss Minimization. 752-761 - Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra:
Large-scale randomized-coordinate descent methods with non-separable linear constraints. 762-771 - Khaled S. Refaat, Adnan Darwiche:
An Upper Bound on the Global Optimum in Parameter Estimation. 772-781 - Kurt Routley, Oliver Schulte:
A Markov Game Model for Valuing Player Actions in Ice Hockey. 782-791 - Amirreza Shaban, Mehrdad Farajtabar, Bo Xie, Le Song, Byron Boots:
Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method. 792-801 - Ilya Shpitser, Karthika Mohan, Judea Pearl:
Missing Data as a Causal and Probabilistic Problem. 802-811 - Anshumali Shrivastava, Ping Li:
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS). 812-821 - Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen:
Learning Optimal Chain Graphs with Answer Set Programming. 822-831 - Milan Studený:
How matroids occur in the context of learning Bayesian network structure. 832-841 - Liessman Sturlaugson, John W. Sheppard:
The Long-Run Behavior of Continuous Time Bayesian Networks. 842-851 - Wen Sun, J. Andrew Bagnell:
Online Bellman Residual Algorithms with Predictive Error Guarantees. 852-861 - Danica J. Sutherland, Jeff G. Schneider:
On the Error of Random Fourier Features. 862-871 - Alex Tank, Nicholas J. Foti, Emily B. Fox:
Bayesian Structure Learning for Stationary Time Series. 872-881 - Johannes Textor, Alexander Idelberger, Maciej Liskiewicz:
Learning from Pairwise Marginal Independencies. 882-891 - Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. 892-901 - Wei Wang, Stuart Russell:
A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC. 902-911 - Erwin Walraven, Matthijs T. J. Spaan:
Planning under Uncertainty with Weighted State Scenarios. 912-921 - Xuezhi Wang, Jeff G. Schneider:
Generalization Bounds for Transfer Learning under Model Shift. 922-931 - Yu Wang, David P. Wipf, Jeong-Min Yun, Wei Chen, Ian J. Wassell:
Clustered Sparse Bayesian Learning. 932-941 - Adrian Weller:
Bethe and Related Pairwise Entropy Approximations. 942-951 - Jason Xu, Vladimir N. Minin:
Effcient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing. 952-961 - Chunlai Zhou, Yuan Feng:
Extend Transferable Belief Models with Probabilistic Priors. 962-971 - Yun Zhou, Norman E. Fenton, Timothy M. Hospedales, Martin Neil:
Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints. 972-981
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