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cs.LG2026
Generalized Random Direction Newton Algorithms for Stochastic Optimization
Soumen Pachal, Prashanth L. A., Shalabh Bhatnagar +1
We present a family of generalized Hessian estimators of the objective using random direction stochastic approximation (RDSA) by utilizing only noisy function measurements. The for…
cs.LG2025
Policy Newton methods for Distortion Riskmetrics
Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A
We consider the problem of risk-sensitive control in a reinforcement learning (RL) framework. In particular, we aim to find a risk-optimal policy by maximizing the distortion riskm…
cs.LG2025
A Finite-Sample Analysis of an Actor-Critic Algorithm for Mean-Variance Optimization in a Discounted MDP
Tejaram Sangadi, L. A. Prashanth, Krishna Jagannathan
Motivated by applications in risk-sensitive reinforcement learning, we study mean-variance optimization in a discounted reward Markov Decision Process (MDP). Specifically, we analy…