4 citations · 9 across the 4 of their papers we have counts for
4 papers
Causal Bandits without Graph Learning
Mikhail Konobeev, Jalal Etesami, Negar Kiyavash
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions. We…
Trace norm regularization for multi-task learning with scarce data
Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples. Motivated by the recent success of neural networks applied to data-s…
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…
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…