Randomly pivoted Cholesky (RPCholesky) is an algorithm for constructing a low-rank
approximation of a positive-semidefinite matrix using a small number of columns. This paper …

Active learning parallelization is widely used, but typically relies on fixing the batch size
throughout experimentation. This fixed approach is inefficient because of a dynamic trade-off …

Column selection is an essential tool for structure-preserving low-rank approximation, with
wide-ranging applications across many fields, such as data science, machine learning, and …

Modern compression methods can summarize a target distribution $\mathbb {P} $ more
succinctly than iid sampling but require access to a low-bias input sequence like a Markov …

Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement
learning tasks. Our aim in this paper is to select a small but representative subset of a large …

Discretization of probability measures is ubiquitous in the field of applied mathematics, from
classical numerical integration to data compression and algorithmic acceleration in machine …