2 citations · 2 across the 8 of their papers we have counts for
9 papers
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…
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…
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
Meltem Tatlı, Arpan Mukherjee, Prashanth L. A. +2
The objective of canonical multi-armed bandits is to identify and repeatedly select an arm with the largest reward, often in the form of the expected value of the arm's probability…
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…
Concentration Bounds for Optimized Certainty Equivalent Risk Estimation
Ayon Ghosh, L. A. Prashanth, Krishna Jagannathan
We consider the problem of estimating the Optimized Certainty Equivalent (OCE) risk from independent and identically distributed (i.i.d.) samples. For the classic sample average ap…
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj +1
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging. We derive finite time bounds on the parameter error…
A Survey of Risk-Aware Multi-Armed Bandits
Vincent Y. F. Tan, Prashanth L. A., Krishna Jagannathan
In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a dru…
A policy gradient approach for optimization of smooth risk measures
Nithia Vijayan, Prashanth L. A
We propose policy gradient algorithms for solving a risk-sensitive reinforcement learning (RL) problem in on-policy as well as off-policy settings. We consider episodic Markov deci…
Estimation of Spectral Risk Measures
Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat
We consider the problem of estimating a spectral risk measure (SRM) from i.i.d. samples, and propose a novel method that is based on numerical integration. We show that our SRM est…