Prashanth L.A.
Associate Professor
SSB 314,
Computer Science and Engineering,
Indian Institute of Technology Madras
Chennai 600036
Email: prashla AT cse.iitm.ac.in
Tel: +91-44-22574377
Research Interests
Reinforcement Learning, Simulation Optimization, Multi-armed Bandits
News
Jan-2023: A paper entitled A policy gradient approach for optimization of smooth risk measures accepted for publication in UAI.
Feb-2023: Invited talk on ‘Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation’ at Networks Seminar Series held (in-person) at Indian Institute of Science. Click here for the video.
Feb-2023: Tutorial on risk-sensitive reinforcement learning at AAAI-2023. Click here for details.
Jan-2023: A paper entitled Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation accepted for publication in AISTATS.
Jan-2023: Teaching a course on stochastic optimization. For details, click here.
Jan-2023: Invited talk on ‘A Wasserstein distance approach for concentration of empirical risk estimates’ at Information Theory and Data Science Workshop held (in-person) at National University of Singapore.
Aug-2022: A paper entitled A Wasserstein distance approach for concentration of empirical risk estimates accepted for publication in Journal of Machine Learning Research.
Jul-2022: Teaching a course on programming and data structures. For details, click here.
Jul-2022: Tutorial on Risk-Aware Multi-armed Bandits at SPCOM 2022. Slides here.
Jun-2022: A monograph entitled Risk-Sensitive Reinforcement Learning via Policy Gradient Search published by Foundations and Trends in Machine Learning.
Apr-2022: A survey article entitled A Survey of Risk-Aware Multi-Armed Bandits accepted at IJCAI-2022.
Feb-2022: Invited talk on ‘Concentration of risk measures: A Wasserstein distance approach’ at ‘IITB Workshop on Stochastic Models’.
Jan-2022: Teaching a course on object oriented analysis using C++. For details, click here.
Oct-2021: Invited talk on ‘Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling’ at IISc workshop on Deep Reinforcement Learning. For the video recording, click here.
Aug-2021: Teaching a course on RL. For details, click here.
Aug-2021: A paper entitled Non-asymptotic bounds for stochastic optimization with biased noisy gradient oracles accepted with minor revisions for publication in IEEE Transactions on Automatic Control.
Aug-2021: Serving on the 'Senior Program Committee’ of AAAI-22.
Jul-2021: Nirav Bhavsar wins ‘Biswajit Sain MS Thesis Award 2021’.
Jul-2021: A paper entitled Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint accepted for publication in Systems and Control Letters.
Feb-2021: Teaching a course on RL. Programming assignments facilitated by Aicrowd - see here, here, and here. For course details, click here.