140 citations · 1.3k across the 125 of their papers we have counts for
9 papers · 1 filter
Hierarchical Bayesian Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +1
Meta-, multi-task, and federated learning can be all viewed as solving similar tasks, drawn from a distribution that reflects task similarities. We provide a unified view of all th…
Safe Data Collection for Offline and Online Policy Learning
Ruihao Zhu, Branislav Kveton
Motivated by practical needs of experimentation and policy learning in online platforms, we study the problem of safe data collection. Specifically, our goal is to develop a loggin…
Optimal Probing with Statistical Guarantees for Network Monitoring at Scale
Muhammad Jehangir Amjad, Christophe Diot, Dimitris Konomis +3
Cloud networks are difficult to monitor because they grow rapidly and the budgets for monitoring them are limited. We propose a framework for estimating network metrics, such as la…
No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer +1
We propose , a Thompson sampling algorithm that adapts sequentially to bandit tasks that it interacts with. The key idea in is to adapt to an unknown tas…
Random Effect Bandits
Rong Zhu, Branislav Kveton
This paper studies regret minimization in a multi-armed bandit. It is well known that side information, such as the prior distribution of arm means in Thompson sampling, can improv…
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…