2 citations · 3 across the 5 of their papers we have counts for
6 papers
Descriptive History Representations: Learning Representations by Answering Questions
Guy Tennenholtz, Jihwan Jeong, Chih-Wei Hsu +2
Effective decision making in partially observable environments requires compressing long interaction histories into informative representations. We introduce Descriptive History Re…
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
Ori Linial, Guy Tennenholtz, Uri Shalit
In many reinforcement learning (RL) applications one cannot easily let the agent act in the world; this is true for autonomous vehicles, healthcare applications, and even some reco…
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Jihwan Jeong, Yinlam Chow, Guy Tennenholtz +4
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditiona…
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of th…
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf +2
A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the obse…
Reinforcement Learning with History-Dependent Dynamic Contexts
Guy Tennenholtz, Nadav Merlis, Lior Shani +2
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP…