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(Pronounced generally as Saw-ruv and Show-oo-rob in Bengali.) Official Email • Personal Email I am a third year Ph.D student in the Computer Science department, at the University of Colorado at Boulder advised by Prof. Lijun Chen. I am interested in exploring the mathematics of algorithms and strategies for decision-making under uncertainty. Specifically, I work in the areas of online learning and reinforcement learning. I graduated with a master's degree in computer science from the same university. 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 and Lijun Chen Accepted at IEEE American Control Conference (ACC) 2024 in Toronto, Canada. conference version / arXiv We explore incentivized exploration in the multiarmed bandit (MAB) problem with changing reward distributions and biased feedback. Our algorithms address abruptly and continuously changing environments, achieving sublinear regret and compensation, effectively incentivizing exploration despite challenges. |
 
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Sourav Chakraborty Master's Thesis, defended on April 2022. Committee: Lijun Chen, Raf Frongillo, Bo Waggoner. thesis / slides We explore incentivized exploration in the multiarmed bandit (MAB) problem with changing reward distributions and biased feedback. Our algorithms address abruptly and continuously changing environments, achieving sublinear regret and compensation, effectively incentivizing exploration despite challenges. |
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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: Sep 2024
Thanks Jon Barron!