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I am a second year Ph.D. student in the Computer Science Department at University of Texas at Austin, working with Prof. Amy Zhang and Prof. Peter Stone. I completed my Dual Degree (B.Tech. + M.Tech.) in Computer Science and Engineering from the Indian Institute of Technology Kharagpur with a department rank 1. My research interests include goal conditioned reinforcement learning and representation learning. I like to view reinforcement learning from state-visitation distributions perspective and look to extend RL as a distribution matching problem. I am also interested in principled algoritms for learning state abstractions or representations that are useful for unknown downstream tasks.
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Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang NeurIPS 2023 project page / arXiv / code We introduce a general framework for goal conditioned RL using f-divergences. |
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Siddhant Agarwal, Aaron Courville, Rishabh Agarwal NeurIPS 2021 workshop on Deep Reinforcement Learning and Ecological Theory of RL project page / paper / We introduce latent representations that can predict the behavior of the agent at future steps to improve generalization in RL. |
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Siddhant Agarwal, Owais Iqbal, Sree Aditya Buridi, Mada Manjusha, Abir Das NeurIPS 2021 workshop on eXplainable AI approaches for debugging and diagnosis project page / arXiv / code We reformulate the process of generating saliency maps using perturbation based methods for black box models as a Markov Decsion Process and use RL to optimally search for the best saliency map, thereby reducing the inference time without hurting the performance. |
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Minjie Sun, Siddhant Agarwal, Zico Kolter ICLR 2021 workshop on Security and Safety in Machine Learning systems. project page / arXiv / code We show that backdoored classifiers can be attacked by anyone rather than only the adversary. We propose an attack that generates alternate triggers for the poisoned classifiers. |
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Mrigank Raman, Aaron Chan*, Siddhant Agarwal*, Peifeng Wang, Hansen Wang, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren * denotes equal contribution International Conference of Learned Representations 2021 and NeurIPS 2020 workshop on Knowledge Representation and Reasoning in Machine Learning [Best paper nomination] project page / arXiv / code We show that using reinforcement learning (or even simple heuristics) we can produce deceptively perturbed knowledge graphs that preserve the downstream performance of the kg-aumented models. |
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Rishabh Madan*, Deepank Agrawal*, Shreyas Kowshik*, Harsh Maheshwari*, Siddhant Agarwal*, Debashish Chakravarty International Conference on Pattern Recognition Application and Methods, Prague, 2019 project page / paper / We use a hybrid CNN archticture that uses two image processing features to classify the images. The CNN architecture has significantly less number of parameters than any of the state of the art methods on GTSRB. |
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project page / We worked to develop vision, planning and localization modules for a category 4 autonomous vehicle. |
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project page / report / code We investigate commonly used cluster management systems like SLURM and Condor. We further develop a fault-tolerant active-passive SLURM-like cluster management system. |
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project page / report / code We use parallization in a GPU to accelarate graph algorithms like BFS, DFS and Single Source Shortest Path and All Pair Shortest Path to achieve massive speedups. |
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project page / presentation / code We develop an android chatbot application using a trained LSTM based encoder-decoder module trained for emotion specific chats. We use a simple classifier to choose the appropriate model from the chat. |
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Webpage template courtesy: Jon Barron |