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ORCID29th UAI 2013: Bellevue, WA, USA
- Ann E. Nicholson, Padhraic Smyth:
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013. AUAI Press 2013 - Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen:
A Sound and Complete Algorithm for Learning Causal Models from Relational Data. - Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang:
Active Learning with Expert Advice. - Sheeraz Ahmad, Angela J. Yu:
Active Sensing as Bayes-Optimal Sequential Decision Making. - James Cussens, Mark Bartlett:
Advances in Bayesian Network Learning using Integer Programming. - Charles Tripp, Ross D. Shachter:
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs. - Patrice Perny, Paul Weng, Judy Goldsmith, Josiah Hanna:
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes. - Hung Bui, Tuyen N. Huynh, Sebastian Riedel:
Automorphism Groups of Graphical Models and Lifted Variational Inference. - Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan P. How:
Batch-iFDD for Representation Expansion in Large MDPs. - Chao Zhang:
Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence. - Qiang Fu, Huahua Wang, Arindam Banerjee:
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference. - Nicholas Ruozzi:
Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function. - Jakramate Bootkrajang, Ata Kabán:
Boosting in the presence of label noise. - Luis Gustavo Vianna, Scott Sanner, Leliane Nunes de Barros:
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs. - Ravi Ganti, Alexander G. Gray:
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens. - Peter Spirtes:
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. - Sanghack Lee, Vasant G. Honavar:
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. - Akshat Kumar, Daniel Sheldon, Biplav Srivastava:
Collective Diffusion Over Networks: Models and Inference. - Oluwasanmi Koyejo, Joydeep Ghosh:
Constrained Bayesian Inference for Low Rank Multitask Learning. - Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Relaxations of Bregman Divergence Clustering. - Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. - Amar Shah, Zoubin Ghahramani:
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering. - Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. - Deepak Venugopal, Vibhav Gogate:
Dynamic Blocking and Collapsing for Gibbs Sampling. - Brandon M. Malone, Changhe Yuan:
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks. - Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths:
Evaluating computational models of explanation using human judgments. - Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini:
Finite-Time Analysis of Kernelised Contextual Bandits. - Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. - James Hensman, Nicoló Fusi, Neil D. Lawrence:
Gaussian Processes for Big Data. - Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte:
Generative Multiple-Instance Learning Models For Quantitative Electromyography. - Krishnakumar Balasubramanian, Kai Yu, Tong Zhang:
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. - Byron Boots, Geoffrey J. Gordon, Arthur Gretton:
Hilbert Space Embeddings of Predictive State Representations. - Stephen H. Bach, Bert Huang, Ben London, Lise Getoor:
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. - Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. - Pengtao Xie, Eric P. Xing:
Integrating Document Clustering and Topic Modeling. - Jean Honorio, Tommi S. Jaakkola:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. - Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. - James McInerney, Alex Rogers, Nicholas R. Jennings:
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries. - Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. - Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. - Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Modeling Documents with Deep Boltzmann Machines. - Zohar Feldman, Carmel Domshlak:
Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency. - Hossein Hajimirsadeghi, Jinling Li, Greg Mori, Mohamed H. Zaki, Tarek Sayed:
Multiple Instance Learning by Discriminative Training of Markov Networks. - Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. - Adrian Weller, Tony Jebara:
On MAP Inference by MWSS on Perfect Graphs. - Denis Deratani Mauá, Cassio Polpo de Campos, Alessio Benavoli, Alessandro Antonucci:
On the Complexity of Strong and Epistemic Credal Networks. - Krikamol Muandet, Bernhard Schölkopf:
One-Class Support Measure Machines for Group Anomaly Detection. - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Optimization With Parity Constraints: From Binary Codes to Discrete Integration. - Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, Patrick Jaillet:
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. - Sigal Oren, Michael Schapira, Moshe Tennenholtz:
Pay or Play. - Krishnendu Chatterjee, Martin Chmelik:
POMDPs under Probabilistic Semantics. - Hossein Azari Soufiani, David C. Parkes, Lirong Xia:
Preference Elicitation For General Random Utility Models. - Damien Bigot, Bruno Zanuttini, Hélène Fargier, Jérôme Mengin:
Probabilistic Conditional Preference Networks. - Aristide C. Y. Tossou, Christos Dimitrakakis:
Probabilistic inverse reinforcement learning in unknown environments. - Nicolas Drougard, Florent Teichteil-Königsbuch, Jean-Loup Farges, Didier Dubois:
Qualitative Possibilistic Mixed-Observable MDPs. - Vaishak Belle, Hector J. Levesque:
Reasoning about Probabilities in Dynamic Systems using Goal Regression. - Emma Brunskill, Lihong Li:
Sample Complexity of Multi-task Reinforcement Learning. - Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano:
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. - Giorgos Borboudakis, Ioannis Tsamardinos:
Scoring and Searching over Bayesian Networks with Causal and Associative Priors. - Marek Petrik, Dharmashankar Subramanian, Janusz Marecki:
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation. - Arindam Khaled, Eric A. Hansen, Changhe Yuan:
Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search. - Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins:
Sparse Nested Markov models with Log-linear Parameters. - Eliot Brenner, David A. Sontag:
SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. - Yaniv Tenzer, Gal Elidan:
Speedy Model Selection (SMS) for Copula Models. - Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng:
Stochastic Rank Aggregation. - Kiyohito Nagano, Yoshinobu Kawahara:
Structured Convex Optimization under Submodular Constraints. - Vibhav Gogate, Pedro M. Domingos:
Structured Message Passing. - Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht:
The Bregman Variational Dual-Tree Framework. - Rishabh K. Iyer, Jeff A. Bilmes:
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking. - Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani:
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. - Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss:
Tighter Linear Program Relaxations for High Order Graphical Models. - Teppo Niinimaki, Mikko Koivisto:
Treedy: A Heuristic for Counting and Sampling Subsets. - Yonatan Halpern, David A. Sontag:
Unsupervised Learning of Noisy-Or Bayesian Networks. - Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani:
Warped Mixtures for Nonparametric Cluster Shapes.
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last updated on 2024-12-03 20:15 CET by the dblp team
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