MAC Design for Heterogeneous Application Support in OFDM Based Wireless Systems. - Prashanth L. A.; Sajal Kumar Das; K. Gopinath (CCNC, 2008)

OFDM-MAC algorithms and their impact on TCP performance in next generation mobile networks. - Prashanth L. A.; K. Gopinath (COMSWARE, 2008)

Stochastic Optimization for Adaptive Labor Staffing in Service Systems. - Prashanth L. A.; H. L. Prasad; Nirmit Desai; Shalabh Bhatnagar; Gargi Banerjee Dasgupta (ICSOC, 2011)

Reinforcement learning with average cost for adaptive control of traffic lights at intersections. - Prashanth L. A.; Shalabh Bhatnagar (ITSC, 2011)

Reinforcement Learning With Function Approximation for Traffic Signal Control. - Prashanth L. A.; Shalabh Bhatnagar (IEEE Trans. Intell. Transp. Syst., 2011)

Threshold Tuning Using Stochastic Optimization for Graded Signal Control. - Prashanth L. A.; Shalabh Bhatnagar (IEEE Trans. Veh. Technol., 2012)

Mechanisms for hostile agents with capacity constraints. - Prashanth Lakshmanrao Ananthapadmanabharao; Horabailu Laxminarayana Prasad; Nirmit Desai; Shalabh Bhatnagar (AAMAS, 2013)

Actor-Critic Algorithms for Risk-Sensitive MDPs. - Prashanth L. A.; Mohammad Ghavamzadeh (NIPS, 2013)

Reinforcement Learning for Sleep-Wake Scheduling in Sensor Networks. - Prashanth Lakshmanrao Ananthapadmanabharao; Abhranil Chatterjee 002; Shalabh Bhatnagar (CoRR, 2013)

Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits. - Nathaniel Korda; Prashanth L. A.; Rémi Munos (CoRR, 2013)

Analysis of stochastic approximation for efficient least squares regression and LSTD. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (CoRR, 2013)

Policy Gradients for CVaR-Constrained MDPs. - Prashanth L. A. (ALT, 2014)

Simultaneous perturbation algorithms for batch off-policy search. - Raphael Fonteneau; Prashanth L. A. (CDC, 2014)

Adaptive sleep-wake control using reinforcement learning in sensor networks. - Prashanth L. A.; Abhranil Chatterjee 002; Shalabh Bhatnagar (COMSNETS, 2014)

Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (ECML/PKDD (2), 2014)

Actor-Critic Algorithms for Risk-Sensitive Reinforcement Learning. - Prashanth L. A.; Mohammad Ghavamzadeh (CoRR, 2014)

Algorithms for Nash Equilibria in General-Sum Stochastic Games. - H. L. Prasad; Prashanth L. A.; Shalabh Bhatnagar (CoRR, 2014)

Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks. - Prashanth L. A.; Abhranil Chatterjee 002; Shalabh Bhatnagar (Wirel. Networks, 2014)

Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. - Nathaniel Korda; Prashanth L. A.; Rémi Munos (AAAI, 2015)

Two-Timescale Algorithms for Learning Nash Equilibria in General-Sum Stochastic Games. - H. L. Prasad; Prashanth L. A.; Shalabh Bhatnagar (AAMAS, 2015)

On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. - Nathaniel Korda; Prashanth L. A. (ICML, 2015)

Adaptive system optimization using (simultaneous) random directions stochastic approximation. - Prashanth L. A.; Shalabh Bhatnagar (CoRR, 2015)

Cumulative Prospect Theory Meets Reinforcement Learning: Estimation and Control. - Prashanth L. A.; Cheng Jie; Michael C. Fu 001; Steven I. Marcus (CoRR, 2015)

Simultaneous Perturbation Newton Algorithms for Simulation Optimization. - Shalabh Bhatnagar; Prashanth L. A. (J. Optim. Theory Appl., 2015)

Simultaneous perturbation methods for adaptive labor staffing in service systems. - Prashanth L. A.; H. L. Prasad; Nirmit Desai; Shalabh Bhatnagar; Gargi Dasgupta (Simul., 2015)

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. - Xiaowei Hu 009; Prashanth L. A.; András György 001; Csaba Szepesvári (AISTATS, 2016)

Improved Hessian estimation for adaptive random directions stochastic approximation. - Sai Koti Reddy Danda; Prashanth L. A.; Shalabh Bhatnagar (CDC, 2016)

Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. - Prashanth L. A.; Cheng Jie; Michael C. Fu 001; Steven I. Marcus; Csaba Szepesvári (ICML, 2016)

Variance-constrained actor-critic algorithms for discounted and average reward MDPs. - Prashanth L. A.; Mohammad Ghavamzadeh (Mach. Learn., 2016)

A constrained optimization perspective on actor-critic algorithms and application to network routing. - Prashanth L. A.; H. L. Prasad; Shalabh Bhatnagar; Prakash Chandra (Syst. Control. Lett., 2016)

Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected. - Aditya Gopalan; Prashanth L. A.; Michael C. Fu 001; Steven I. Marcus (AAAI, 2017)

Adaptive System Optimization Using Random Directions Stochastic Approximation. - Prashanth L. A.; Shalabh Bhatnagar; Michael C. Fu 001; Steven I. Marcus (IEEE Trans. Autom. Control., 2017)

Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint. - Prashanth L. A.; Michael C. Fu 001 (CoRR, 2018)

Stochastic Optimization in a Cumulative Prospect Theory Framework. - Cheng Jie; Prashanth L. A.; Michael C. Fu 001; Steven I. Marcus; Csaba Szepesvári (IEEE Trans. Autom. Control., 2018)

Correlated bandits or: How to minimize mean-squared error online. - Vinay Praneeth Boda; Prashanth L. A. (ICML, 2019)

Concentration of risk measures: A Wasserstein distance approach. - Sanjay P. Bhat; Prashanth L. A. (NeurIPS, 2019)

Risk-aware Multi-armed Bandits Using Conditional Value-at-Risk. - Ravi Kumar Kolla; Prashanth L. A.; Krishna P. Jagannathan (CoRR, 2019)

Improved Concentration Bounds for Conditional Value-at-Risk and Cumulative Prospect Theory using Wasserstein distance. - Sanjay P. Bhat; Prashanth L. A. (CoRR, 2019)

Concentration bounds for empirical conditional value-at-risk: The unbounded case. - Ravi Kumar Kolla; Prashanth L. A.; Sanjay P. Bhat; Krishna P. Jagannathan (Oper. Res. Lett., 2019)

Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. - Prashanth L. A.; Krishna P. Jagannathan; Ravi Kumar Kolla (ICML, 2020)

Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization. - Nirav Bhavsar; Prashanth L. A. (CoRR, 2020)

Random Directions Stochastic Approximation With Deterministic Perturbations. - Prashanth L. A.; Shalabh Bhatnagar; Nirav Bhavsar; Michael C. Fu 001; Steven I. Marcus (IEEE Trans. Autom. Control., 2020)

Estimation of Spectral Risk Measures. - Ajay Kumar Pandey; Prashanth L. A.; Sanjay P. Bhat (AAAI, 2021)

Smoothed functional-based gradient algorithms for off-policy reinforcement learning. - Nithia Vijayan; Prashanth L. A. (CoRR, 2021)

Likelihood ratio-based policy gradient methods for distorted risk measures: A non-asymptotic analysis. - Nithia Vijayan; Prashanth L. A. (CoRR, 2021)

Online Estimation and Optimization of Utility-Based Shortfall Risk. - Arvind S. Menon; Prashanth L. A.; Krishna P. Jagannathan (CoRR, 2021)

Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. - Prashanth L. A.; Nathaniel Korda; Rémi Munos (Mach. Learn., 2021)

Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint. - Nithia Vijayan; Prashanth L. A. (Syst. Control. Lett., 2021)

A Survey of Risk-Aware Multi-Armed Bandits. - Vincent Y. F. Tan; Prashanth L. A.; Krishna P. Jagannathan (IJCAI, 2022)

Approximate gradient ascent methods for distortion risk measures. - Nithia Vijayan; Prashanth L. A. (CoRR, 2022)

Adaptive Estimation of Random Vectors with Bandit Feedback. - Dipayan Sen; Prashanth L. A.; Aditya Gopalan (CoRR, 2022)

A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization. - Akash Mondal; Prashanth L. A.; Shalabh Bhatnagar (CoRR, 2022)

Risk-Sensitive Reinforcement Learning via Policy Gradient Search. - Prashanth L. A.; Michael C. Fu 001 (Found. Trends Mach. Learn., 2022)

A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates. - Prashanth L. A.; Sanjay P. Bhat (J. Mach. Learn. Res., 2022)

Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. - Gandharv Patil; Prashanth L. A.; Dheeraj Nagaraj; Doina Precup (AISTATS, 2023)

Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. - Shalabh Bhatnagar; Prashanth L. A. (CISS, 2023)

A policy gradient approach for optimization of smooth risk measures. - Nithia Vijayan; Prashanth L. A. (UAI, 2023)

VaR and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds. - Sanjay Bhat; Prashanth L. A.; Gugan Thoppe (CoRR, 2023)

Nonasymptotic Bounds for Stochastic Optimization With Biased Noisy Gradient Oracles. - Nirav Bhavsar; Prashanth L. A. (IEEE Trans. Autom. Control., 2023)

A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. - Mizhaan Prajit Maniyar; Prashanth L. A.; Akash Mondal; Shalabh Bhatnagar (AISTATS, 2024)

Optimization of Utility-based Shortfall Risk: A Non-asymptotic Viewpoint. - Sumedh Gupte; Prashanth L. A.; Sanjay P. Bhat (CDC, 2024)

Policy Evaluation for Variance in Average Reward Reinforcement Learning. - Shubhada Agrawal; Prashanth L. A.; Siva Theja Maguluri (ICML, 2024)

Risk Estimation in a Markov Cost Process: Lower and Upper Bounds. - Gugan Thoppe; Prashanth L. A.; Sanjay P. Bhat (ICML, 2024)

Truncated Cauchy random perturbations for smoothed functional-based stochastic optimization. - Akash Mondal; Prashanth L. A.; Shalabh Bhatnagar (Autom., 2024)

Concentration Bounds for Optimized Certainty Equivalent Risk Estimation. - Ayon Ghosh; Prashanth L. A.; Krishna P. Jagannathan (CoRR, 2024)

Finite Time Analysis of Temporal Difference Learning for Mean-Variance in a Discounted MDP. - Tejaram Sangadi; Prashanth L. A.; Krishna P. Jagannathan (CoRR, 2024)

Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. - Meltem Tatli; Arpan Mukherjee; Prashanth L. A.; Karthikeyan Shanmugam; Ali Tajer (AISTATS, 2025)

Gradient-Based Algorithms for Zeroth-Order Optimization. - Prashanth L. A.; Shalabh Bhatnagar (Found. Trends Optim., 2025)

Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms. - Meltem Tatli; Arpan Mukherjee; Prashanth L. A.; Karthikeyan Shanmugam; Ali Tajer (CoRR, 2025)

Learning to optimize convex risk measures: The cases of utility-based shortfall risk and optimized certainty equivalent risk. - Sumedh Gupte; Prashanth L. A.; Sanjay P. Bhat (CoRR, 2025)

Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias. - Soumen Pachal; Shalabh Bhatnagar; Prashanth L. A. (IEEE Trans. Autom. Control., 2025)

Policy Newton methods for Distortion Riskmetrics. - Soumen Pachal; Mizhaan Prajit Maniyar; Prashanth L. A. (CoRR, 2025)