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An Adaptive Strategy for Active Learning with Smooth Decision Boundary
The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this setting.
Adaptivity to Noise Parameters in Nonparametric Active Learning
This work addresses various open questions in the theory of active learning for nonparametric classification.
Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem
We consider the problem of \textit{best arm identification} with a \textit{fixed budget $T$}, in the $K$-armed stochastic bandit setting, with arms distribution defined on $[0, 1]$.
An optimal algorithm for the Thresholding Bandit Problem
We study a specific \textit{combinatorial pure exploration stochastic bandit problem} where the learner aims at finding the set of arms whose means are above a given threshold, up to a given precision, and \textit{for a fixed time horizon}.
Rotting bandits are not harder than stochastic ones
In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary.
