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)
22722a343513ed45f14905eb07621686-Paper.pdf
Neural Information Processing Systems
We analyze the complexity of learning directed acyclic graphical models from observational dataingeneral settings without specific distributional assumptions.
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DeepSynopticMonteCarloPlanningin ReconnaissanceBlindChess
Neural Information Processing Systems
This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games.
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Observable
Neural Information Processing Systems
The concept of information structure is also fundamental to studying the phenomenon ofpartial observability.
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