24 citations · 37 across the 10 of their papers we have counts for
10 papers
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
Meltem Tatlı, Arpan Mukherjee, Prashanth L. A. +2
This paper introduces a general framework for risk-sensitive bandits that integrates the notions of risk-sensitive objectives by adopting a rich class of distortion riskmetrics. Th…
Optimization of utility-based shortfall risk: A non-asymptotic viewpoint
Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
We consider the problems of estimation and optimization of utility-based shortfall risk (UBSR), which is a popular risk measure in finance. In the context of UBSR estimation, we de…
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
Mizhaan Prajit Maniyar, Akash Mondal, Prashanth L. A. +1
We consider the problem of control in the setting of reinforcement learning (RL), where model information is not available. Policy gradient algorithms are a popular solution approa…
Non-asymptotic bounds for stochastic optimization with biased noisy gradient oracles
Nirav Bhavsar, Prashanth L. A
We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter. Our propos…
A Wasserstein distance approach for concentration of empirical risk estimates
Prashanth L. A., Sanjay P. Bhat
This paper presents a unified approach based on Wasserstein distance to derive concentration bounds for empirical estimates for two broad classes of risk measures defined in the pa…
Concentration bounds for empirical conditional value-at-risk: The unbounded case
Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat +1
In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control loss…
A constrained optimization perspective on actor critic algorithms and application to network routing
Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar +1
We propose a novel actor-critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent directi…
Adaptive system optimization using random directions stochastic approximation
Prashanth L. A., Shalabh Bhatnagar, Michael Fu +1
We present novel algorithms for simulation optimization using random directions stochastic approximation (RDSA). These include first-order (gradient) as well as second-order (Newto…
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Nathaniel Korda, L. A. Prashanth
We provide non-asymptotic bounds for the well-known temporal difference learning algorithm TD(0) with linear function approximators. These include high-probability bounds as well a…
Actor-Critic Algorithms for Learning Nash Equilibria in N-player General-Sum Games
H. L Prasad, L. A. Prashanth, Shalabh Bhatnagar
We consider the problem of finding stationary Nash equilibria (NE) in a finite discounted general-sum stochastic game. We first generalize a non-linear optimization problem from Fi…