bayesian-risk

This paper introduces sparse priors to improve theoretical guarantees on distribution learning, showing that under a sparse prior, the sample complexity scales as Ω(√(k/n)) instead of O(n^{-1/Θ(d)}), thereby mitigating the curse of dimensionality.

This paper proposes a quantile Bayesian risk-aware MDP framework for online RL that adaptively balances robustness and exploration over time, providing theoretical regret bounds and demonstrating strong empirical performance.