56 citations · 64 across the 11 of their papers we have counts for
11 papers
Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applications
Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
We consider the optimization of the Optimized Certainty Equivalent (OCE) risk, with applications including portfolio optimization in finance, and uncertainty quantification, classi…
Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning
Ankur Naskar, Vivek T A, Aditya Kumar +2
Discounted exponential utility provides a principled criterion for risk-sensitive sequential decision-making, but its nonlinear structure complicates reinforcement learning. A rece…
Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs
Gugan Thoppe, L. A. Prashanth, Ankur Naskar +1
Reinforcement learning (RL) for exponential-utility optimization in discounted Markov decision processes (MDPs) lacks principled value-based algorithms. We address this gap in the…
Reinforcement learning with an expectile-based objective
Sumedh Gupte, Shrey Rakeshkumar Patel, Soumen Pachal +2
We consider the policy evaluation and control in a finite horizon reinforcement learning (RL) setting under an expectile-based objective. First, we derive the mean-squared error (M…
Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
Gugan Thoppe, L. A. Prashanth, Sanjay Bhat
We tackle the problem of estimating risk measures of the infinite-horizon discounted cost within a Markov cost process. The risk measures we study include variance, Value-at-Risk (…
A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization
Akash Mondal, Prashanth L. A., Shalabh Bhatnagar
In this paper, we present a stochastic gradient algorithm for minimizing a smooth objective function that is an expectation over noisy cost samples, and only the latter are observe…
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint
Nithia Vijayan, Prashanth L. A
We propose two policy gradient algorithms for solving the problem of control in an off-policy reinforcement learning (RL) context. Both algorithms incorporate a smoothed functional…
Correlated bandits or: How to minimize mean-squared error online
Vinay Praneeth Boda, Prashanth L. A
While the objective in traditional multi-armed bandit problems is to find the arm with the highest mean, in many settings, finding an arm that best captures information about other…
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Prashanth L. A., Krishna Jagannathan, Ravi Kumar Kolla
Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases o…
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Xiaowei Hu, Prashanth L. A., András György +1
Algorithms for bandit convex optimization and online learning often rely on constructing noisy gradient estimates, which are then used in appropriately adjusted first-order algorit…
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L. A., Cheng Jie, Michael Fu +2
Cumulative prospect theory (CPT) is known to model human decisions well, with substantial empirical evidence supporting this claim. CPT works by distorting probabilities and is mor…