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ORCIDPrashanth L. A.
Prashanth Lakshmanrao Ananthapadmanabharao
Person information
- affiliation: University of Maryland
- affiliation: INRIA Lille - Nord Europe
- affiliation: Indian Institute of Science, Department of Computer Science and Automation
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2020 – today
- 2024
[j17]Akash Mondal, Prashanth L. A., Shalabh Bhatnagar:
Truncated Cauchy random perturbations for smoothed functional-based stochastic optimization. Autom. 162: 111528 (2024)- [c28]Mizhaan Prajit Maniyar, Prashanth L. A., Akash Mondal, Shalabh Bhatnagar:
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. AISTATS 2024: 4708-4716 - [c27]Shubhada Agrawal, Prashanth L. A., Siva Theja Maguluri:
Policy Evaluation for Variance in Average Reward Reinforcement Learning. ICML 2024 - [c26]Gugan Thoppe, Prashanth L. A., Sanjay P. Bhat:
Risk Estimation in a Markov Cost Process: Lower and Upper Bounds. ICML 2024 - [i36]Ayon Ghosh, Prashanth L. A., Krishna P. Jagannathan:
Concentration Bounds for Optimized Certainty Equivalent Risk Estimation. CoRR abs/2405.20933 (2024) - [i35]Tejaram Sangadi, Prashanth L. A., Krishna P. Jagannathan:
Finite Time Analysis of Temporal Difference Learning for Mean-Variance in a Discounted MDP. CoRR abs/2406.07892 (2024) - 2023
- [j16]Nirav Bhavsar
, Prashanth L. A.:
Nonasymptotic Bounds for Stochastic Optimization With Biased Noisy Gradient Oracles. IEEE Trans. Autom. Control. 68(3): 1628-1641 (2023) - [c25]Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup:
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. AISTATS 2023: 5438-5448 - [c24]Shalabh Bhatnagar, Prashanth L. A.:
Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. CISS 2023: 1-6 - [c23]Nithia Vijayan, Prashanth L. A.:
A policy gradient approach for optimization of smooth risk measures. UAI 2023: 2168-2178 - [i34]Mizhaan Prajit Maniyar, Akash Mondal, Prashanth L. A., Shalabh Bhatnagar:
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. CoRR abs/2304.10951 (2023) - [i33]Sanjay Bhat, Prashanth L. A., Gugan Thoppe:
VaR\ and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds. CoRR abs/2310.11389 (2023) - [i32]Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat:
Optimization of utility-based shortfall risk: A non-asymptotic viewpoint. CoRR abs/2310.18743 (2023) - 2022
- [j15]Prashanth L. A., Michael C. Fu:
Risk-Sensitive Reinforcement Learning via Policy Gradient Search. Found. Trends Mach. Learn. 15(5): 537-693 (2022) - [j14]Prashanth L. A., Sanjay P. Bhat:
A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates. J. Mach. Learn. Res. 23: 238:1-238:61 (2022) - [c22]Vincent Y. F. Tan, Prashanth L. A., Krishna P. Jagannathan:
A Survey of Risk-Aware Multi-Armed Bandits. IJCAI 2022: 5623-5629 - [i31]Nithia Vijayan, Prashanth L. A.:
Approximate gradient ascent methods for distortion risk measures. CoRR abs/2202.11046 (2022) - [i30]Dipayan Sen, Prashanth L. A., Aditya Gopalan:
Adaptive Estimation of Random Vectors with Bandit Feedback. CoRR abs/2203.16810 (2022) - [i29]Vincent Y. F. Tan, Prashanth L. A., Krishna P. Jagannathan:
A Survey of Risk-Aware Multi-Armed Bandits. CoRR abs/2205.05843 (2022) - [i28]Akash Mondal, Prashanth L. A., Shalabh Bhatnagar:
A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization. CoRR abs/2208.00290 (2022) - [i27]Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup:
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. CoRR abs/2210.05918 (2022) - [i26]Shalabh Bhatnagar, Prashanth L. A.:
Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. CoRR abs/2212.10477 (2022) - 2021
- [j13]Prashanth L. A., Nathaniel Korda, Rémi Munos:
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. Mach. Learn. 110(3): 559-618 (2021) - [j12]Nithia Vijayan, Prashanth L. A.:
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint. Syst. Control. Lett. 155: 104988 (2021) - [c21]Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat:
Estimation of Spectral Risk Measures. AAAI 2021: 12166-12173 - [i25]Nithia Vijayan, Prashanth L. A.:
Smoothed functional-based gradient algorithms for off-policy reinforcement learning. CoRR abs/2101.02137 (2021) - [i24]Nithia Vijayan, Prashanth L. A.:
Likelihood ratio-based policy gradient methods for distorted risk measures: A non-asymptotic analysis. CoRR abs/2107.04422 (2021) - [i23]Arvind S. Menon, Prashanth L. A., Krishna P. Jagannathan:
Online Estimation and Optimization of Utility-Based Shortfall Risk. CoRR abs/2111.08805 (2021) - 2020
- [j11]Prashanth L. A., Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven I. Marcus:
Random Directions Stochastic Approximation With Deterministic Perturbations. IEEE Trans. Autom. Control. 65(6): 2450-2465 (2020) - [c20]Prashanth L. A., Krishna P. Jagannathan, Ravi Kumar Kolla:
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. ICML 2020: 5577-5586 - [i22]Nirav Bhavsar, Prashanth L. A.:
Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization. CoRR abs/2002.11440 (2020)
2010 – 2019
- 2019
- [j10]Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat, Krishna P. Jagannathan:
Concentration bounds for empirical conditional value-at-risk: The unbounded case. Oper. Res. Lett. 47(1): 16-20 (2019) - [c19]Vinay Praneeth Boda, Prashanth L. A.:
Correlated bandits or: How to minimize mean-squared error online. ICML 2019: 686-694 - [c18]Sanjay P. Bhat, Prashanth L. A.:
Concentration of risk measures: A Wasserstein distance approach. NeurIPS 2019: 11739-11748 - [i21]Ravi Kumar Kolla, Prashanth L. A., Krishna P. Jagannathan:
Risk-aware Multi-armed Bandits Using Conditional Value-at-Risk. CoRR abs/1901.00997 (2019) - [i20]Vinay Praneeth Boda, Prashanth L. A.:
Correlated bandits or: How to minimize mean-squared error online. CoRR abs/1902.02953 (2019) - [i19]Sanjay P. Bhat, Prashanth L. A.:
Improved Concentration Bounds for Conditional Value-at-Risk and Cumulative Prospect Theory using Wasserstein distance. CoRR abs/1902.10709 (2019) - [i18]Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat:
Estimation of Spectral Risk Measures. CoRR abs/1912.10398 (2019) - 2018
- [j9]Cheng Jie, Prashanth L. A., Michael C. Fu, Steven I. Marcus, Csaba Szepesvári:
Stochastic Optimization in a Cumulative Prospect Theory Framework. IEEE Trans. Autom. Control. 63(9): 2867-2882 (2018) - [i17]Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat, Krishna P. Jagannathan:
Concentration bounds for empirical conditional value-at-risk: The unbounded case. CoRR abs/1808.01739 (2018) - [i16]Prashanth L. A., Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven I. Marcus:
Random directions stochastic approximation with deterministic perturbations. CoRR abs/1808.02871 (2018) - [i15]Prashanth L. A., Michael C. Fu:
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint. CoRR abs/1810.09126 (2018) - 2017
- [j8]Prashanth L. A., Shalabh Bhatnagar, Michael C. Fu, Steven I. Marcus:
Adaptive System Optimization Using Random Directions Stochastic Approximation. IEEE Trans. Autom. Control. 62(5): 2223-2238 (2017) - [c17]Aditya Gopalan, Prashanth L. A., Michael C. Fu, Steven I. Marcus:
Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected. AAAI 2017: 1941-1947 - 2016
- [j7]Prashanth L. A., Mohammad Ghavamzadeh:
Variance-constrained actor-critic algorithms for discounted and average reward MDPs. Mach. Learn. 105(3): 367-417 (2016) - [j6]Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar, Prakash Chandra:
A constrained optimization perspective on actor-critic algorithms and application to network routing. Syst. Control. Lett. 92: 46-51 (2016) - [c16]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS 2016: 819-828 - [c15]Sai Koti Reddy Danda, Prashanth L. A., Shalabh Bhatnagar:
Improved Hessian estimation for adaptive random directions stochastic approximation. CDC 2016: 3682-3687 - [c14]Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus, Csaba Szepesvári:
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. ICML 2016: 1406-1415 - [i14]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR abs/1609.07087 (2016) - [i13]Aditya Gopalan, Prashanth L. A., Michael C. Fu, Steven I. Marcus:
Weighted bandits or: How bandits learn distorted values that are not expected. CoRR abs/1611.10283 (2016) - 2015
- [j5]Shalabh Bhatnagar, Prashanth L. A.:
Simultaneous Perturbation Newton Algorithms for Simulation Optimization. J. Optim. Theory Appl. 164(2): 621-643 (2015) - [j4]Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Dasgupta:
Simultaneous perturbation methods for adaptive labor staffing in service systems. Simul. 91(5): 432-455 (2015) - [c13]Nathaniel Korda, Prashanth L. A., Rémi Munos:
Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. AAAI 2015: 2708-2714 - [c12]H. L. Prasad, Prashanth L. A., Shalabh Bhatnagar:
Two-Timescale Algorithms for Learning Nash Equilibria in General-Sum Stochastic Games. AAMAS 2015: 1371-1379 - [c11]Nathaniel Korda, Prashanth L. A.:
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. ICML 2015: 626-634 - [i12]Prashanth L. A., Shalabh Bhatnagar:
Adaptive system optimization using (simultaneous) random directions stochastic approximation. CoRR abs/1502.05577 (2015) - [i11]Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus:
Cumulative Prospect Theory Meets Reinforcement Learning: Estimation and Control. CoRR abs/1506.02632 (2015) - [i10]Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar, Prakash Chandra:
A constrained optimization perspective on actor critic algorithms and application to network routing. CoRR abs/1507.07984 (2015) - 2014
- [j3]Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar:
Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks. Wirel. Networks 20(8): 2589-2604 (2014) - [c10]Prashanth L. A.:
Policy Gradients for CVaR-Constrained MDPs. ALT 2014: 155-169 - [c9]Raphael Fonteneau, Prashanth L. A.:
Simultaneous perturbation algorithms for batch off-policy search. CDC 2014: 2622-2627 - [c8]Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar:
Adaptive sleep-wake control using reinforcement learning in sensor networks. COMSNETS 2014: 1-8 - [c7]Prashanth L. A., Nathaniel Korda, Rémi Munos:
Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control. ECML/PKDD (2) 2014: 66-81 - [i9]H. L. Prasad, Prashanth L. A., Shalabh Bhatnagar:
Algorithms for Nash Equilibria in General-Sum Stochastic Games. CoRR abs/1401.2086 (2014) - [i8]Raphael Fonteneau, Prashanth L. A.:
Simultaneous Perturbation Algorithms for Batch Off-Policy Search. CoRR abs/1403.4514 (2014) - [i7]Prashanth L. A., Mohammad Ghavamzadeh:
Actor-Critic Algorithms for Risk-Sensitive Reinforcement Learning. CoRR abs/1403.6530 (2014) - [i6]Prashanth L. A.:
Policy Gradients for CVaR-Constrained MDPs. CoRR abs/1405.2690 (2014) - [i5]Nathaniel Korda, Prashanth L. A.:
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. CoRR abs/1411.3224 (2014) - 2013
- [c6]Prashanth Lakshmanrao Ananthapadmanabharao, Horabailu Laxminarayana Prasad, Nirmit Desai, Shalabh Bhatnagar:
Mechanisms for hostile agents with capacity constraints. AAMAS 2013: 659-666 - [c5]Prashanth L. A., Mohammad Ghavamzadeh:
Actor-Critic Algorithms for Risk-Sensitive MDPs. NIPS 2013: 252-260 - [i4]Prashanth L. A., Nathaniel Korda, Rémi Munos:
Analysis of stochastic approximation for efficient least squares regression and LSTD. CoRR abs/1306.2557 (2013) - [i3]Nathaniel Korda, Prashanth L. A., Rémi Munos:
Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits. CoRR abs/1307.3176 (2013) - [i2]Prashanth Lakshmanrao Ananthapadmanabharao, Abhranil Chatterjee, Shalabh Bhatnagar:
Reinforcement Learning for Sleep-Wake Scheduling in Sensor Networks. CoRR abs/1312.7292 (2013) - [i1]Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Dasgupta:
Simultaneous Perturbation Methods for Adaptive Labor Staffing in Service Systems. CoRR abs/1312.7430 (2013) - 2012
- [j2]Prashanth L. A., Shalabh Bhatnagar:
Threshold Tuning Using Stochastic Optimization for Graded Signal Control. IEEE Trans. Veh. Technol. 61(9): 3865-3880 (2012) - 2011
- [j1]Prashanth L. A., Shalabh Bhatnagar:
Reinforcement Learning With Function Approximation for Traffic Signal Control. IEEE Trans. Intell. Transp. Syst. 12(2): 412-421 (2011) - [c4]Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Banerjee Dasgupta:
Stochastic Optimization for Adaptive Labor Staffing in Service Systems. ICSOC 2011: 487-494 - [c3]Prashanth L. A., Shalabh Bhatnagar:
Reinforcement learning with average cost for adaptive control of traffic lights at intersections. ITSC 2011: 1640-1645
2000 – 2009
- 2008
- [c2]Prashanth L. A., Sajal Kumar Das, K. Gopinath:
MAC Design for Heterogeneous Application Support in OFDM Based Wireless Systems. CCNC 2008: 412-413 - [c1]Prashanth L. A., K. Gopinath:
OFDM-MAC algorithms and their impact on TCP performance in next generation mobile networks. COMSWARE 2008: 133-140
Coauthor Index
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last updated on 2024-09-13 01:40 CEST by the dblp team
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