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)
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees (with Supplementary Material)
Neural Information Processing Systems
Supplementary material is at the end of this document.
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Reward is Enough for Convex MDPs
Neural Information Processing Systems
Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
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Reward is Enough for Convex MDPs
Neural Information Processing Systems
Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
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ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Neural Information Processing Systems
We conduct various experiments to test ProtoX.
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