| 2026 | AAAI | Policy Newton Methods for Distortion Riskmetrics. | Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A. |
| 2025 | AISTATS | Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. | Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer |
| 2024 | AISTATS | A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. | Mizhaan Prajit Maniyar, Prashanth L. A., Akash Mondal, Shalabh Bhatnagar |
| 2024 | ICML | Policy Evaluation for Variance in Average Reward Reinforcement Learning. | Shubhada Agrawal, Prashanth L. A., Siva Theja Maguluri |
| 2024 | ICML | Risk Estimation in a Markov Cost Process: Lower and Upper Bounds. | Gugan Thoppe, Prashanth L. A., Sanjay P. Bhat |
| 2023 | AISTATS | Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. | Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup |
| 2023 | CISS | Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias. | Shalabh Bhatnagar, Prashanth L. A. |
| 2023 | UAI | A policy gradient approach for optimization of smooth risk measures. | Nithia Vijayan, Prashanth L. A. |
| 2022 | IJCAI | A Survey of Risk-Aware Multi-Armed Bandits. | Vincent Y. F. Tan, Prashanth L. A., Krishna P. Jagannathan |
| 2021 | AAAI | Estimation of Spectral Risk Measures. | Ajay Kumar Pandey, Prashanth L. A., Sanjay P. Bhat |
| 2020 | ICML | Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. | Prashanth L. A., Krishna P. Jagannathan, Ravi Kumar Kolla |
| 2019 | ICML | Correlated bandits or: How to minimize mean-squared error online. | Vinay Praneeth Boda, Prashanth L. A. |
| 2017 | AAAI | Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected. | Aditya Gopalan, Prashanth L. A., Michael C. Fu, Steven I. Marcus |
| 2016 | AISTATS | (Bandit) Convex Optimization with Biased Noisy Gradient Oracles. | Xiaowei Hu, Prashanth L. A., Andrs Gyrgy, Csaba Szepesvri |
| 2016 | ICML | Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. | Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus, Csaba Szepesvri |
| 2015 | AAAI | Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits. | Nathaniel Korda, Prashanth L. A., Rmi Munos |
| 2015 | ICML | On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. | Nathaniel Korda, Prashanth L. A. |
| 2014 | ALT | Policy Gradients for CVaR-Constrained MDPs. | Prashanth L. A. |
| 2014 | COMSNETS | Adaptive sleep-wake control using reinforcement learning in sensor networks. | Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar |
| 2011 | ICSOC | Stochastic Optimization for Adaptive Labor Staffing in Service Systems. | Prashanth L. A., H. L. Prasad, Nirmit Desai, Shalabh Bhatnagar, Gargi Banerjee Dasgupta |