Guy Tennenholtz | DeepAI

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∙ 09/08/2023

Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models

Modern recommender systems lie at the heart of complex ecosystems that c...

0 Craig Boutilier, et al. ∙

∙ 06/02/2023

A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits

Algorithms for offline bandits must optimize decisions in uncertain envi...

0 Mohammad Ghavamzadeh, et al. ∙

∙ 06/01/2023

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

A prominent challenge of offline reinforcement learning (RL) is the issu...

0 Alizée Pace, et al. ∙

∙ 05/31/2023

Representation-Driven Reinforcement Learning

We present a representation-driven framework for reinforcement learning....

0 Ofir Nabati, et al. ∙

∙ 05/24/2023

Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics

While popularity bias is recognized to play a role in recommmender (and ...

0 Guy Tennenholtz, et al. ∙

∙ 02/04/2023

Reinforcement Learning with History-Dependent Dynamic Contexts

We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a no...

0 Guy Tennenholtz, et al. ∙

∙ 05/30/2022

Reinforcement Learning with a Terminator

We present the problem of reinforcement learning with exogenous terminat...

0 Guy Tennenholtz, et al. ∙

∙ 10/13/2021

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

We consider the problem of using expert data with unobserved confounders...

0 Guy Tennenholtz, et al. ∙

∙ 09/22/2021

Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (MARL) faces significant ...

0 Roy Zohar, et al. ∙

∙ 03/18/2021

Maximum Entropy Reinforcement Learning with Mixture Policies

Mixture models are an expressive hypothesis class that can approximate a...

0 Nir Baram, et al. ∙

∙ 02/22/2021

Action Redundancy in Reinforcement Learning

Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning p...

0 Nir Baram, et al. ∙

∙ 02/22/2021

GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning

Offline reinforcement learning approaches can generally be divided to pr...

0 Guy Tennenholtz, et al. ∙

∙ 07/26/2020

The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks

We identify a fundamental phenomenon of heterogeneous one dimensional ra...

0 Asaf Cassel, et al. ∙

∙ 06/11/2020

Bandits with Partially Observable Offline Data

We study linear contextual bandits with access to a large, partially obs...

0 Guy Tennenholtz, et al. ∙

∙ 10/02/2019

Natural Language State Representation for Reinforcement Learning

Recent advances in Reinforcement Learning have highlighted the difficult...

0 Erez Schwartz, et al. ∙

∙ 09/09/2019

Off-Policy Evaluation in Partially Observable Environments

This work studies the problem of batch off-policy evaluation for Reinfor...

0 Guy Tennenholtz, et al. ∙

∙ 05/23/2019

Distributional Policy Optimization: An Alternative Approach for Continuous Control

We identify a fundamental problem in policy gradient-based methods in co...

0 Chen Tessler, et al. ∙

∙ 02/04/2019

The Natural Language of Actions

We introduce Act2Vec, a general framework for learning context-based act...

0 Guy Tennenholtz, et al. ∙

∙ 02/16/2018

Train on Validation: Squeezing the Data Lemon

Model selection on validation data is an essential step in machine learn...

0 Guy Tennenholtz, et al. ∙

∙ 11/22/2017

The Stochastic Firefighter Problem

The dynamics of infectious diseases spread is crucial in determining the...

0 Guy Tennenholtz, et al. ∙

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