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
Neural Information Processing Systems
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
Neural Information Processing Systems
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
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Recently, Sherman et al. [2023a] proposed a
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