140 citations · 1.3k across the 125 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…
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu, Branislav Kveton, Zheng Wen +2
We propose a novel framework for structured bandits, which we call an influence diagram bandit. Our framework captures complex statistical dependencies between actions, latent vari…
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…
Sample Efficient Graph-Based Optimization with Noisy Observations
Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori +2
We study sample complexity of optimizing "hill-climbing friendly" functions defined on a graph under noisy observations. We define a notion of convexity, and we show that a variant…
Non-Stationary Off-Policy Optimization
Joey Hong, Branislav Kveton, Manzil Zaheer +2
Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-s…
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…