Random Projection

Practically and provably efficient RL under resource constraints!

Practically and provably efficient RL under resource constraints!

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.

Hypermodel for efficient incremental approximation of the posterior (uncertainty quantification) over complex models without leveraging conjugacy as encountering more data; Index sampling for approximate posterior sampling for data-efficient sequential decision-making. This approach is provably superior than ensemble sampling and Langevin Monte-carlo.

We present a simple and unified analysis of the Johnson-Lindenstrauss (JL) lemma, a cornerstone in the field of dimensionality …

Addressing efficiency chanllenge in RL by HyperFQI algorithm

Addressing efficiency challenge in RL with theoretical advancements and practical algorithm designs.