1.1k citations · 4.8k across the 64 of their papers we have counts for
4 papers · 1 filter
Thompson Sampling with a Mixture Prior
Joey Hong, Branislav Kveton, Manzil Zaheer +2
We study Thompson sampling (TS) in online decision making, where the uncertain environment is sampled from a mixture distribution. This is relevant in multi-task learning, where a…
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
Ruohan Zhan, Konstantina Christakopoulou, Ya Le +6
Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content…
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain +7
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in…
Meta-Thompson Sampling
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer +4
Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit insta…