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)
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Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
Neural Information Processing Systems
These matrix ensembles are defined precisely in section 2.1, but the reader can keep in
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Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Max Simchowitz, Kevin G. Jamieson
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Neural Information Processing Systems http://nips.cc/
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Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Neural Information Processing Systems
Many real datasets are hierarchically structured.
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Localized Structured Prediction
Carlo Ciliberto, Francis Bach, Alessandro Rudi
Neural Information Processing Systems
Neural Information Processing Systems http://nips.cc/
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