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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Offline Multitask Representation Learning for Reinforcement Learning
Neural Information Processing Systems
We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning.
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Trust Y our: Gradient-based Intervention Targeting for Causal Discovery
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Often, observational data alone is not enough to uniquely identify a system's causal structure.
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9dc5accb1e4f4a9798eae145f2e4869b-Paper-Conference.pdf
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Both challenges involve modeling long-term dependencies.
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