all AI news
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. (arXiv:2202.10589v4 [stat.ML] UPDATED)
Aug. 4, 2022, 1:11 a.m. | Chengchun Shi, Jin Zhu, Ye Shen, Shikai Luo, Hongtu Zhu, Rui Song
stat.ML updates on arXiv.org arxiv.org
This paper is concerned with constructing a confidence interval for a target
policy's value offline based on a pre-collected observational data in infinite
horizon settings. Most of the existing works assume no unmeasured variables
exist that confound the observed actions. This assumption, however, is likely
to be violated in real applications such as healthcare and technological
industries. In this paper, we show that with some auxiliary variables that
mediate the effect of actions on the system dynamics, the target policy's …
More from arxiv.org / stat.ML updates on arXiv.org
Learning linear dynamical systems under convex constraints
2 days, 21 hours ago |
arxiv.org
Inverse Unscented Kalman Filter
3 days, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne