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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Policy Optimization for Robust Average Cost MDPs
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Specifically, we focus on ergodic Markov chains.
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Learning Mixtures of Unknown Causal Interventions
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The ability to conduct interventions plays a pivotal role in learning causal relationships among variables, thus facilitating applications across diverse scientific disciplines such as genomics, economics, and machine learning.
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No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery Alex Rutherford Michael Beukman Timon Willi Bruno Lacerda Nick Hawes Jakob Foerster University of Oxford
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Put differently, current methods fail to predict intuitive measures of "learnability."
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