Temporal Inference with Finite Factored Sets
We propose a new approach to temporal inference, inspired by the Pearlian causal inference paradigm - though quite different from Pearl's approach formally. Rather than using directed acyclic graphs, we make use of factored sets, which are sets expressed as Cartesian products. We show that finite factored sets are powerful tools for inferring temporal relations. We introduce an analog of d-separation for factored sets, conditional orthogonality, and we demonstrate that this notion is equivalent to conditional independence in all probability distributions on a finite factored set.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceSimilar Papers 제목 키워드 기반
Lazy Factored Inference for Functional Probabilistic Programming
Probabilistic programming provides the means to represent and reason about complex probabilistic models using programming language constructs. Even simple probabilistic programs can produce models with infinitely many va…
Probabilistic ProgrammingLeveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent factorization structure, resulting in a pote…
Offline RLreinforcement-learningReinforcement Learning (RL)Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
We study the projected gradient descent method on low-rank matrix problems with a strongly convex objective. We use the Burer-Monteiro factorization approach to implicitly enforce low-rankness; such factorization introdu…
LEMMAQuantum State TomographyRetrievalFactored Value Functions for Graph-Based Multi-Agent Reinforcement Learning
Credit assignment is a core challenge in multi-agent reinforcement learning (MARL), especially in large-scale systems with structured, local interactions. Graph-based Markov decision processes (GMDPs) capture such settin…
Multi-agent Reinforcement LearningStructured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work w…
Probabilistic Programming