50 citations · 108 across the 18 of their papers we have counts for
3 papers · 1 filter
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat +3
Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In re…
Meta-Learning Bandit Policies by Gradient Ascent
Branislav Kveton, Martin Mladenov, Chih-Wei Hsu +3
Most bandit policies are designed to either minimize regret in any problem instance, making very few assumptions about the underlying environment, or in a Bayesian sense, assuming…
Differentiable Bandit Exploration
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton +3
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution . In this work, we learn such policies for an…