2 papers
cs.LG2021
Meta-Thompson Sampling
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer +4
Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit insta…
stat.ML2020
A Distribution-Dependent Analysis of Meta-Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvári
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the…