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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Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Neural Information Processing Systems
Code can be found at https://github.com/nsidn98/LLaMAR
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Provable Partially Observable Reinforcement Learning with Privileged Information Yang Cai
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Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).
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QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
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Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learning causal graphs in Linear Gaussian Acyclic Models (LiGAMs), where the permutation encodes a causal ordering of the variables. Existing methods in this setting do not scale due to their high computational complexity.
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Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
Neural Information Processing Systems
Recently, Sherman et al. [2023a] proposed a
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