2024

  1. A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization
    Akash Mondal, Prashanth L.A., Shalabh Bhatnagar
    Automatica

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

  3. VaR and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds
    Sanjay Bhat, Prashanth L.A., Gugan Thoppe
    Under review.

  4. Optimization of utility-based shortfall risk: A non-asymptotic viewpoint
    Sumedh Gupte, Prashanth L.A., Sanjay Bhat
    Under review.

  5. Online Estimation and Optimization of Utility-Based Shortfall Risk
    Vishwajit Hegde, Arvind S. Menon, Prashanth L.A., Krishna Jagannathan
    Under review.

  6. Generalized Simultaneous Perturbation-based Gradient Search with Reduced Estimator Bias
    Soumen Pachal, S.Bhatnagar and Prashanth L.A.
    Under review.

  7. Adaptive Estimation of Random Vectors with Bandit Feedback: A mean-squared error viewpoint
    Dipayan Sen, Prashanth L.A., Aditya Gopalan
    Under review.

2023

  1. Adaptive Estimation of Random Vectors with Bandit Feedback
    Dipayan Sen, Prashanth L.A., Aditya Gopalan
    ICC

  2. A policy gradient approach for optimization of smooth risk measures
    N. Vijayan and Prashanth L.A.
    UAI

  3. Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias
    S.Bhatnagar and Prashanth L.A.
    CISS

  4. 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

  5. Bandit algorithms to emulate human decision making using probabilistic distortions
    Ravi Kumar Kolla, Prashanth L.A., Aditya Gopalan, Krishna Jagannathan, Michael Fu, Steve Marcus
    Draft.

2022

  1. A Wasserstein distance approach for concentration of empirical risk estimates
    Prashanth L.A. and Sanjay P. Bhat
    Journal of Machine Learning Research

  2. Risk-Sensitive Reinforcement Learning via Policy Gradient Search
    Prashanth L.A. and Michael Fu
    Foundations and Trends in Machine Learning

  3. A Survey of Risk-Aware Multi-Armed Bandits
    Vincent Y. F. Tan, Prashanth L.A., and Krishna Jagannathan
    International Joint Conference on Artificial Intelligence (IJCAI) (Survey track)

  4. Non-asymptotic bounds for stochastic optimization with biased noisy gradient oracles
    Nirav Bhavsar and Prashanth L.A.
    IEEE Transactions on Automatic Control

2021

  1. Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint
    N. Vijayan and Prashanth L.A.
    Systems & Control Letters

  2. Estimation of Spectral Risk Measures
    Ajay Kumar Pandey, Prashanth L.A. and Sanjay P. Bhat
    AAAI

  3. Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
    Prashanth L.A., Nathaniel Korda and Remi Munos
    Machine Learning

2020

  1. Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
    Prashanth L.A., Krishna Jagannathan and Ravi Kumar Kolla
    International Conference on Machine Learning (ICML)

  2. Random directions stochastic approximation with deterministic perturbations
    Prashanth L.A., S.Bhatnagar, Nirav Bhavsar, Michael Fu and Steve Marcus
    IEEE Transactions on Automatic Control

2019

  1. Concentration of risk measures: A Wasserstein distance approach
    Sanjay P. Bhat and Prashanth L.A.
    Neural Information Processing Systems (NeurIPS)

  2. Correlated bandits or: How to minimize mean-squared error online
    V.P. Boda and Prashanth L.A.
    International Conference on Machine Learning (ICML)

  3. Concentration bounds for empirical conditional value-at-risk: The unbounded case
    Ravi Kumar Kolla, Prashanth L.A., Sanjay P. Bhat, Krishna Jagannathan
    Operations Research Letters

2018

  1. Stochastic optimization in a cumulative prospect theory framework
    Jie Cheng, Prashanth L.A., Michael Fu, Steve Marcus and Csaba Szepesvari
    IEEE Transactions on Automatic Control, Vol. 63, No. 9, pp. 2867-2882.

2017

  1. Adaptive system optimization using random directions stochastic approximation
    Prashanth L.A., S.Bhatnagar, Michael Fu and Steve Marcus
    IEEE Transactions on Automatic Control, Vol. 62, Issue 5, pp.2223–2238.

  2. Weighted bandits or: How bandits learn distorted values that are not expected
    Aditya Gopalan, Prashanth L.A., Michael Fu and Steve Marcus
    AAAI Conference on Artificial Intelligence

2016

  1. (Bandit) Convex Optimization with Biased Noisy Gradient Oracles
    Xiaowei Hu, Prashanth L.A., Andras Gyorgy and Csaba Szepesvari
    Draft.

  2. Improved Hessian estimation for adaptive random directions stochastic approximation
    D. Sai Koti Reddy, Prashanth L.A. and S.Bhatnagar
    IEEE Conference on Decision and Control (CDC)

  3. Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
    Prashanth L.A., Jie Cheng, Michael Fu, Steve Marcus and Csaba Szepesvari
    International Conference on Machine Learning (ICML)

  4. Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs
    Prashanth L.A. and Mohammad Ghavamzadeh
    Machine Learning

  5. A constrained optimization perspective on actor critic algorithms and application to network routing
    Prashanth L.A., H.L.Prasad, S.Bhatnagar and Prakash Chandra
    Systems & Control Letters.

  6. (Bandit) Convex Optimization with Biased Noisy Gradient Oracles
    Xiaowei Hu, Prashanth L.A., Andras Gyorgy and Csaba Szepesvari
    International Conference on Artificial Intelligence and Statistics (AISTATS)

2015

  1. On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
    Nathaniel Korda and Prashanth L.A.
    International Conference on Machine Learning (ICML)
    [Proof has a bug, rendering the bounds invalid. A fix will happen later than sooner..]

  2. Two-Timescale Algorithms for Learning Nash Equilibria in General-Sum Stochastic Games
    H.L.Prasad, Prashanth L.A. and S.Bhatnagar
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

  3. Fast gradient descent for drifting least squares regression, with application to bandits
    Nathaniel Korda, Prashanth L.A. and Remi Munos
    AAAI Conference on Artificial Intelligence

  4. Simultaneous Perturbation Methods for Adaptive Labor Staffing in Service Systems
    Prashanth L.A., H.L.Prasad, N.Desai, S.Bhatnagar and G.Dasgupta
    Simulation, DOI: 10.1177/0037549715581198, pp. 1-24.

  5. Simultaneous Perturbation Newton Algorithms for Simulation Optimization
    S.Bhatnagar and Prashanth L.A.
    Journal of Optimization Theory and Applications, Vol. 164, Issue. 2, pp. 621-643.

  6. Actor-Critic Algorithms for Learning Nash Equilibria in N-player General-Sum Games
    Prashanth L.A., H.L.Prasad and S.Bhatnagar
    Draft

2014

  1. Simultaneous Perturbation Algorithms for Batch Off-Policy Search
    Raphael Fonteneau and Prashanth L.A.
    IEEE Conference on Decision and Control (CDC)

  2. Policy Gradients for CVaR-Constrained MDPs
    Prashanth L.A.
    International Conference on Algorithmic Learning Theory (ALT)

  3. Fast LSTD using stochastic approximation: Finite time analysis and application to traffic control
    Prashanth L.A., Nathaniel Korda and Remi Munos
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

  4. Two Timescale Convergent Q-learning for Sleep–Scheduling in Wireless Sensor Networks
    Prashanth L.A., A. Chatterjee and S.Bhatnagar
    Wireless Networks, Vol. 20, Issue. 8, pp. 2589-2604.

2013

  1. Actor-Critic Algorithms for Risk-Sensitive MDPs
    Prashanth L.A. and Mohammad Ghavamzadeh
    Neural Information Processing Systems (NIPS) (Full oral presentation)

  2. Mechanisms for Hostile Agents with Capacity Constraints
    Prashanth L.A., H.L.Prasad, N.Desai and S.Bhatnagar
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

  3. Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods
    S.Bhatnagar, H.L.Prasad and Prashanth L.A.
    Lecture Notes in Control and Information Sciences Series, Vol. 434, Springer, ISBN 978-1-4471-4284-3, Edition: 2013, 302 pages.

  4. Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems
    H.L.Prasad, L.A.Prashanth, S.Bhatnagar and N.Desai
    Service Science (INFORMS), Vol. 5, No. 1, pp. 29-55.

2012

  1. Threshold Tuning using Stochastic Optimization for Graded Signal Control
    Prashanth L.A. and S.Bhatnagar
    IEEE Transactions on Vehicular Technology, Vol. 61, No. 9, pp.3865-3880.

  2. Adaptive feature pursuit: Online adaptation of features in reinforcement learning
    S.Bhatnagar, V.S.Borkar and Prashanth L.A.
    Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (Ed. F. Lewis and D. Liu), IEEE Press Computational Intelligence Series, pp. 517-534

  3. Resource Allocation for Sequential Decision Making under Uncertainty: Studies in Vehicular Traffic Control, Service Systems, Sensor Networks and Mechanism Design
    Prashanth L.A.
    Ph.D. thesis, Indian Institute of Science (IEEE ITSS Best Ph.D. Dissertation 2014 - Third Prize).

2011

  1. Stochastic optimization for adaptive labor staffing in service systems
    Prashanth L.A., H.L.Prasad, N.Desai, S.Bhatnagar and G.Dasgupta
    International Conference on Service Oriented Computing (ICSOC)

  2. Reinforcement Learning with Average Cost for Adaptive Control of Traffic Lights at Intersections
    Prashanth L.A. and S.Bhatnagar
    IEEE Intelligent Transportation Systems Conference

  3. Reinforcement learning with function approximation for traffic signal control
    Prashanth L.A. and S.Bhatnagar
    IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 2, pp.412-421.

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