probability

Has implications on how the integration of prior knowledge enhances the efficiency of RL agents without extensive online exploration.

First probabilistic framework for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty; A non-trivial martingale extension of Johnson-Lindenstrauss (JL) to sequentially adaptive data processes.

TL;DR: We simplify and unify various constructions of the Johnson-Lindenstrauss (JL) lemma, including spherical and sub-Gaussian models, and provide the first rigorous proof for spherical construction’s effectiveness. Our work extends the Hanson-Wright inequality and solidifies the JL lemma’s theoretical foundation, enhancing its practical applications in computational algorithms.