activity
20182026
most citedLearn What Not to Learn: Action Elimination with Deep Reinforcement Learning

125 citations · 148 across the 19 of their papers we have counts for

collaborators
Showing 2023Show all

2 papers · 1 filter

cs.IR2023

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

Guy Tennenholtz, Martin Mladenov, Nadav Merlis +2

While popularity bias is recognized to play a crucial role in recommmender (and other ranking-based) systems, detailed analysis of its impact on collective user welfare has largely…

cs.LG2023

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…