140 citations · 1.3k across the 125 of their papers we have counts for
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Multi-Task Off-Policy Learning from Bandit Feedback
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users…
Bayesian Fixed-Budget Best-Arm Identification
Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi +1
Fixed-budget best-arm identification (BAI) is a bandit problem where the agent maximizes the probability of identifying the optimal arm within a fixed budget of observations. In th…
Robust Contextual Linear Bandits
Rong Zhu, Branislav Kveton
Model misspecification is a major consideration in applications of statistical methods and machine learning. However, it is often neglected in contextual bandits. This paper studie…
From Ranked Lists to Carousels: A Carousel Click Model
Behnam Rahdari, Branislav Kveton, Peter Brusilovsky
Carousel-based recommendation interfaces allow users to explore recommended items in a structured, efficient, and visually-appealing way. This made them a de-facto standard approac…
Uplifting Bandits
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton
We introduce a multi-armed bandit model where the reward is a sum of multiple random variables, and each action only alters the distributions of some of them. After each action, th…
Pessimistic Off-Policy Optimization for Learning to Rank
Matej Cief, Branislav Kveton, Michal Kompan
Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging du…