probability

We prove HyperAgent closes a theoretical gap in scalable exploration. Further, GPT-HyperAgent addresses risk and efficiency challenges in human-Al interplay for automated content moderation with human feedback.

Addressing data and computation efficiency challenges in real-world deployments of RL Agents. It achieves significant efficiency gains in deep RL benchmarks as well as theoretical milestones.

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.