1.1k citations · 4.8k across the 64 of their papers we have counts for
69 papers
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…
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
Siddharth Prasad, Martin Mladenov, Craig Boutilier
Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a compreh…
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…
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du +7
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…
Aligning Text-to-Image Models using Human Feedback
Kimin Lee, Hao Liu, Moonkyung Ryu +6
Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text…
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Dhawal Gupta, Yinlam Chow, Aza Tulepbergenov +2
Reinforcement learning (RL) has shown great promise for developing dialogue management (DM) agents that are non-myopic, conduct rich conversations, and maximize overall user satisf…