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(Pronounced generally as Saw-ruv and Show-oo-rob in Bengali.) Official Email • Personal Email I am a first year Ph.D student in the Computer Science department, at the University of Colorado at Boulder advised by Dr. Lijun Chen. My research interests are reinforcement learning and human-robot interaction. I graduated with a master's degree in computer science from the same institute. Before coming to Boulder, I had worked as a software engineer at Flipkart in India, after graduating from Birla Institute of Technology, Mesra, Ranchi. When not thinking about work, I treat myself to the world of films, books and music. |
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Sourav Chakraborty Master's Thesis, defended on April 2022. Committee: (Advisor) Lijun Chen, Raf Frongillo, Bo Waggoner. ProQuest link / slides We study the incentivized exploration for the multi-armed bandit (MAB) problem with nonstationary reward distributions, where the players receive compensation for exploring arms other than the greedy choice and may provide a biased feedback on reward. We analyze the impact of the drifted reward feedback on two instances of non-stationary MAB environments: Piecewise-Stationary and Continuously-Changing. We show that our algorithms for both the environments achieve sub-linear regret and compensation under drifted reward, and are therefore effective in incentivizing exploration. Experimental results with synthetic data are provided to complement the theoretical analysis. |
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Tuhina Tripathi, Alexa Reed, Sourav Chakraborty April , 2022 report / code / demo / interface-code Final project for ASEN 5519: Decision Making Under Uncertainty. This project explores the use of Inverse Reinforcement Learning, via Maximum Entropy Formulation, in a Markov Decision Process. The concepts explored in this project were demonstrated using a grid world environment. |
 
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Sourav Chakraborty September, 2020 original paper / code Just playing around with the paper by Liu & Wang et alon Incentivized Exploration for Multi-Armed Bandits under Reward Drift where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward drift. |
 
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*Amit Baran Roy, Sourav Chakraborty May, 2020 report / code A word embedding model implementation based on the popular skipgram architecture. It involves alterations of the scoring algorithm to give more weightage to the context words that are closer to the target word in a skipgram sliding window. |
 
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Sourav Chakraborty, Nagarajan Shanmuganathan May, 2020 report / code It is known well that Counterfactual regret minimization (CFR) has been used in games which have both terminal states and perfect recall to minimize regret. This project aims to relax those constraints and use a local no-regret algorithm (LONR) by Kash et al, which internally uses a Q-learning like update rule to games which do not have terminal states or perfect recall. |
 
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*Amit Baran Roy, Aparajita Singh, Sourav Chakraborty, Tanmai Gajula Feb-April, 2020 report / code The complete 3D geometry of an object from a single 2.5D depth view was acquired by using deep learning techniques such as generative adversarial networks and 3D convolution neural networks. The resolution of the final 3D voxelized output was improved by transforming the voxel representation into another representation called occupancy networks. |
Last updated: Nov 14, 2022
Thanks Jon Barron!