Prashanth L. A.

91 papers A* 12A 7B 1Misc 2Journal 61Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Generalized Random Direction Newton Algorithms for Stochastic Optimization. CoRR Soumen Pachal, Prashanth L. A., Shalabh Bhatnagar, Avinash Achar
2026	A*	`conf`	Policy Newton Methods for Distortion Riskmetrics. AAAI Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A.
2026	J	`jnl`	Risk-sensitive reinforcement learning using expectiles, shortfall risk and optimized certainty equivalent risk. CoRR Sumedh Gupte, Shrey Rakeshkumar Patel, Soumen Pachal, Prashanth L. A., Sanjay P. Bhat
2025	J	`jnl`	Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias. IEEE Trans. Autom. Control. Soumen Pachal, Shalabh Bhatnagar, Prashanth L. A.
2025	J	`jnl`	Gradient-Based Algorithms for Zeroth-Order Optimization. Found. Trends Optim. Prashanth L. A., Shalabh Bhatnagar
2025	J	`jnl`	Learning to optimize convex risk measures: The cases of utility-based shortfall risk and optimized certainty equivalent risk. CoRR Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
2025	J	`jnl`	Online Estimation and Optimization of Utility-Based Shortfall Risk. Math. Oper. Res. Vishwajit Hegde, Arvind S. Menon, Prashanth L. A., Krishna P. Jagannathan
2025	J	`jnl`	Policy Newton methods for Distortion Riskmetrics. CoRR Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A.
2025	J	`jnl`	Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms. CoRR Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer
2025	A	`conf`	Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. AISTATS Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer
2025	J	`jnl`	Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. CoRR Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer
2024	A	`conf`	A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. AISTATS Mizhaan Prajit Maniyar, Prashanth L. A., Akash Mondal, Shalabh Bhatnagar
2024	J	`jnl`	Concentration Bounds for Optimized Certainty Equivalent Risk Estimation. CoRR Ayon Ghosh, Prashanth L. A., Krishna P. Jagannathan
2024	J	`jnl`	Finite Time Analysis of Temporal Difference Learning for Mean-Variance in a Discounted MDP. CoRR Tejaram Sangadi, Prashanth L. A., Krishna P. Jagannathan
2024	—	`conf`	Optimization of Utility-based Shortfall Risk: A Non-asymptotic Viewpoint. CDC Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
2024	A*	`conf`	Policy Evaluation for Variance in Average Reward Reinforcement Learning. ICML Shubhada Agrawal, Prashanth L. A., Siva Theja Maguluri
2024	A*	`conf`	Risk Estimation in a Markov Cost Process: Lower and Upper Bounds. ICML Gugan Thoppe, Prashanth L. A., Sanjay P. Bhat
2024	J	`jnl`	Truncated Cauchy random perturbations for smoothed functional-based stochastic optimization. Autom. Akash Mondal, Prashanth L. A., Shalabh Bhatnagar
2023	J	`jnl`	A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. CoRR Mizhaan Prajit Maniyar, Akash Mondal, Prashanth L. A., Shalabh Bhatnagar
2023	A	`conf`	A policy gradient approach for optimization of smooth risk measures. UAI Nithia Vijayan, Prashanth L. A.
2023	A	`conf`	Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. AISTATS Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup
2023	Misc	`conf`	Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. CISS Shalabh Bhatnagar, Prashanth L. A.
2023	J	`jnl`	Nonasymptotic Bounds for Stochastic Optimization With Biased Noisy Gradient Oracles. IEEE Trans. Autom. Control. Nirav Bhavsar, Prashanth L. A.
2023	J	`jnl`	Optimization of utility-based shortfall risk: A non-asymptotic viewpoint. CoRR Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
2023	J	`jnl`	VaR\ and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds. CoRR Sanjay Bhat, Prashanth L. A., Gugan Thoppe
2022	J	`jnl`	A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization. CoRR Akash Mondal, Prashanth L. A., Shalabh Bhatnagar
2022	A*	`conf`	A Survey of Risk-Aware Multi-Armed Bandits. IJCAI Vincent Y. F. Tan, Prashanth L. A., Krishna P. Jagannathan
2022	J	`jnl`	A Survey of Risk-Aware Multi-Armed Bandits. CoRR Vincent Y. F. Tan, Prashanth L. A., Krishna P. Jagannathan
2022	J	`jnl`	A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates. J. Mach. Learn. Res. Prashanth L. A., Sanjay P. Bhat
2022	J	`jnl`	Adaptive Estimation of Random Vectors with Bandit Feedback. CoRR Dipayan Sen, Prashanth L. A., Aditya Gopalan
2022	J	`jnl`	Approximate gradient ascent methods for distortion risk measures. CoRR Nithia Vijayan, Prashanth L. A.
2022	J	`jnl`	Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. CoRR Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup
2022	J	`jnl`	Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. CoRR Shalabh Bhatnagar, Prashanth L. A.
2022	J	`jnl`	Risk-Sensitive Reinforcement Learning via Policy Gradient Search. Found. Trends Mach. Learn. Prashanth L. A., Michael C. Fu
2021	J	`jnl`	Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. Mach. Learn. Prashanth L. A., Nathaniel Korda, Rémi Munos
2021	A*	`conf`	Estimation of Spectral Risk Measures. AAAI Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat
2021	J	`jnl`	Likelihood ratio-based policy gradient methods for distorted risk measures: A non-asymptotic analysis. CoRR Nithia Vijayan, Prashanth L. A.
2021	J	`jnl`	Online Estimation and Optimization of Utility-Based Shortfall Risk. CoRR Arvind S. Menon, Prashanth L. A., Krishna P. Jagannathan
2021	J	`jnl`	Smoothed functional-based gradient algorithms for off-policy reinforcement learning. CoRR Nithia Vijayan, Prashanth L. A.
2021	J	`jnl`	Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint. Syst. Control. Lett. Nithia Vijayan, Prashanth L. A.
2020	A*	`conf`	Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. ICML Prashanth L. A., Krishna P. Jagannathan, Ravi Kumar Kolla
2020	J	`jnl`	Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization. CoRR Nirav Bhavsar, Prashanth L. A.
2020	J	`jnl`	Random Directions Stochastic Approximation With Deterministic Perturbations. IEEE Trans. Autom. Control. Prashanth L. A., Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven I. Marcus
2019	J	`jnl`	Concentration bounds for empirical conditional value-at-risk: The unbounded case. Oper. Res. Lett. Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat, Krishna P. Jagannathan
2019	A*	`conf`	Concentration of risk measures: A Wasserstein distance approach. NeurIPS Sanjay P. Bhat, Prashanth L. A.
2019	A*	`conf`	Correlated bandits or: How to minimize mean-squared error online. ICML Vinay Praneeth Boda, Prashanth L. A.
2019	J	`jnl`	Correlated bandits or: How to minimize mean-squared error online. CoRR Vinay Praneeth Boda, Prashanth L. A.
2019	J	`jnl`	Estimation of Spectral Risk Measures. CoRR Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat
2019	J	`jnl`	Improved Concentration Bounds for Conditional Value-at-Risk and Cumulative Prospect Theory using Wasserstein distance. CoRR Sanjay P. Bhat, Prashanth L. A.
2019	J	`jnl`	Risk-aware Multi-armed Bandits Using Conditional Value-at-Risk. CoRR Ravi Kumar Kolla, Prashanth L. A., Krishna P. Jagannathan
2018	J	`jnl`	Concentration bounds for empirical conditional value-at-risk: The unbounded case. CoRR Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat, Krishna P. Jagannathan
2018	J	`jnl`	Random directions stochastic approximation with deterministic perturbations. CoRR Prashanth L. A., Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven I. Marcus
2018	J	`jnl`	Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint. CoRR Prashanth L. A., Michael C. Fu
2018	J	`jnl`	Stochastic Optimization in a Cumulative Prospect Theory Framework. IEEE Trans. Autom. Control. Cheng Jie, Prashanth L. A., Michael C. Fu, Steven I. Marcus, Csaba Szepesvári
2017	J	`jnl`	Adaptive System Optimization Using Random Directions Stochastic Approximation. IEEE Trans. Autom. Control. Prashanth L. A., Shalabh Bhatnagar, Michael C. Fu, Steven I. Marcus
2017	A*	`conf`	Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected. AAAI Aditya Gopalan, Prashanth L. A., Michael C. Fu, Steven I. Marcus
2016	A	`conf`	(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
2016	J	`jnl`	(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
2016	J	`jnl`	A constrained optimization perspective on actor-critic algorithms and application to network routing. Syst. Control. Lett. Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar, Prakash Chandra
2016	A*	`conf`	Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. ICML Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus, Csaba Szepesvári
2016	—	`conf`	Improved Hessian estimation for adaptive random directions stochastic approximation. CDC Sai Koti Reddy Danda, Prashanth L. A., Shalabh Bhatnagar
2016	J	`jnl`	Variance-constrained actor-critic algorithms for discounted and average reward MDPs. Mach. Learn. Prashanth L. A., Mohammad Ghavamzadeh
2016	J	`jnl`	Weighted bandits or: How bandits learn distorted values that are not expected. CoRR Aditya Gopalan, Prashanth L. A., Michael C. Fu, Steven I. Marcus
2015	J	`jnl`	A constrained optimization perspective on actor critic algorithms and application to network routing. CoRR Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar, Prakash Chandra
2015	J	`jnl`	Adaptive system optimization using (simultaneous) random directions stochastic approximation. CoRR Prashanth L. A., Shalabh Bhatnagar
2015	J	`jnl`	Cumulative Prospect Theory Meets Reinforcement Learning: Estimation and Control. CoRR Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus
2015	A*	`conf`	Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. AAAI Nathaniel Korda, Prashanth L. A., Rémi Munos
2015	A*	`conf`	On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. ICML Nathaniel Korda, Prashanth L. A.
2015	J	`jnl`	Simultaneous Perturbation Newton Algorithms for Simulation Optimization. J. Optim. Theory Appl. Shalabh Bhatnagar, Prashanth L. A.
2015	J	`jnl`	Simultaneous perturbation methods for adaptive labor staffing in service systems. Simul. Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Dasgupta
2015	A	`conf`	Two-Timescale Algorithms for Learning Nash Equilibria in General-Sum Stochastic Games. AAMAS H. L. Prasad, Prashanth L. A., Shalabh Bhatnagar
2014	J	`jnl`	Actor-Critic Algorithms for Risk-Sensitive Reinforcement Learning. CoRR Prashanth L. A., Mohammad Ghavamzadeh
2014	Misc	`conf`	Adaptive sleep-wake control using reinforcement learning in sensor networks. COMSNETS Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar
2014	J	`jnl`	Algorithms for Nash Equilibria in General-Sum Stochastic Games. CoRR H. L. Prasad, Prashanth L. A., Shalabh Bhatnagar
2014	—	`conf`	Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control. ECML/PKDD (2) Prashanth L. A., Nathaniel Korda, Rémi Munos
2014	J	`jnl`	On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. CoRR Nathaniel Korda, Prashanth L. A.
2014	B	`conf`	Policy Gradients for CVaR-Constrained MDPs. ALT Prashanth L. A.
2014	J	`jnl`	Policy Gradients for CVaR-Constrained MDPs. CoRR Prashanth L. A.
2014	J	`jnl`	Simultaneous Perturbation Algorithms for Batch Off-Policy Search. CoRR Raphael Fonteneau, Prashanth L. A.
2014	—	`conf`	Simultaneous perturbation algorithms for batch off-policy search. CDC Raphael Fonteneau, Prashanth L. A.
2014	J	`jnl`	Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks. Wirel. Networks Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar
2013	—	`conf`	Actor-Critic Algorithms for Risk-Sensitive MDPs. NIPS Prashanth L. A., Mohammad Ghavamzadeh
2013	J	`jnl`	Analysis of stochastic approximation for efficient least squares regression and LSTD. CoRR Prashanth L. A., Nathaniel Korda, Rémi Munos
2013	J	`jnl`	Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits. CoRR Nathaniel Korda, Prashanth L. A., Rémi Munos
2013	J	`jnl`	Simultaneous Perturbation Methods for Adaptive Labor Staffing in Service Systems. CoRR Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Dasgupta
2012	J	`jnl`	Threshold Tuning Using Stochastic Optimization for Graded Signal Control. IEEE Trans. Veh. Technol. Prashanth L. A., Shalabh Bhatnagar
2011	J	`jnl`	Reinforcement Learning With Function Approximation for Traffic Signal Control. IEEE Trans. Intell. Transp. Syst. Prashanth L. A., Shalabh Bhatnagar
2011	—	`conf`	Reinforcement learning with average cost for adaptive control of traffic lights at intersections. ITSC Prashanth L. A., Shalabh Bhatnagar
2011	A	`conf`	Stochastic Optimization for Adaptive Labor Staffing in Service Systems. ICSOC Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Banerjee Dasgupta
2008	—	`conf`	MAC Design for Heterogeneous Application Support in OFDM Based Wireless Systems. CCNC Prashanth L. A., Sajal Kumar Das, K. Gopinath
2008	—	`conf`	OFDM-MAC algorithms and their impact on TCP performance in next generation mobile networks. COMSWARE Prashanth L. A., K. Gopinath