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CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions.
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound.
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm
We study the corrupted bandit problem, i. e. a stochastic multi-armed bandit problem with $k$ unknown reward distributions, which are heavy-tailed and corrupted by a history-independent adversary or Nature.
Excess risk bounds in robust empirical risk minimization
We propose a version of empirical risk minimization based on the idea of replacing sample averages by robust proxies of the expectation, and obtain high-confidence bounds for the excess risk of resulting estimators.
