Search Results for author:
Found 5 papers, 4 papers with code
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
However, in the age of large multivariate and incomplete data, statistical dependencies between features must be estimated in a computationally tractable way, while also dealing with missing data.
An Anytime Algorithm for Good Arm Identification
Second, when APGAI is combined with a stopping rule, we prove upper bounds on the expected sampling complexity, holding at any confidence level.
Near-Optimal Collaborative Learning in Bandits
In this paper, we provide new lower bounds on the sample complexity of pure exploration and on the regret.
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem.
Top-m identification for linear bandits
Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m $\ge$ 1 arms with largest means in a linear bandit model, in the fixed-confidence setting.
