arXiv Machine Learning
Jun 18

How fast can you find a good hypothesis?

arXiv:2509. 03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$.

By Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Sandeep Silwal
arXiv Machine Learning
Jun 15

A Complexity Measure for Active Learning in Multi-group Mean Estimation

arXiv:2606. 14690v1 Announce Type: new Abstract: We study a \emph{max-risk} objective for active learning in a multi-group mean estimation $d$-armed bandits: a learner adaptively allocates a budget of $T$ samples across $d$ groups to minimize the worst-case uncertainty index $\max_{k\in[d]}\sigma_k^2/n_k$, where $\sigma_k$ is the standard deviation of the distribution of arm $d$, and $n_k$ is the number of times arm $d$ is sampled.

By Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub