Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
Counting the Optimal Solutions in Graphical Models
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Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger, Thomas B. Schön
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Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
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The Broad Optimality of Profile Maximum Likelihood
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Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu
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Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
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Learning latent variable structured prediction models with Gaussian perturbations
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Parsimonious Bayesian deep networks
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