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)
Efficient Model-Free Exploration in Low-Rank MDPs
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What are the right computational primitives for exploration?
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QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
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Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
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PAC Learning Linear Thresholds from Label Proportions
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The goal is to train a good instance classifier.
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Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
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Please refer to the website for more details.
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Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng Kevin Bello
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These results are then generalized to general models and likelihoods, where the same claims hold.
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ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
Neural Information Processing Systems
In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.
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