1.1k citations · 4.8k across the 64 of their papers we have counts for
7 papers · 1 filter
Non-Stationary Latent Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +4
Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fas…
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…
Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer +3
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to i…
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…
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Andy Su, Jayden Ooi, Tyler Lu +2
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular…
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Ge Liu, Rui Wu, Heng-Tze Cheng +7
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as…