Siddhant Agarwal
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.
Google Scholar / Github / Twitter / LinkedIn/ Email / Resume
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Reinforcement Explanation Learning
Siddhant Agarwal, Owais Iqbal, Sree Aditya Buridi, Mada Manjusha, Abir Das
NeurIPS 2021 workshop on eXplainable AI approaches for debugging and diagnosis
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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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Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbations
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]
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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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Traffic Sign Classification using HOG-SURF features and Convolutional Neural Networks
Rishabh Madan*, Deepank Agrawal*, Shreyas Kowshik*, Harsh Maheshwari*, Siddhant Agarwal*, Debashish Chakravarty
International Conference on Pattern Recognition Application and Methods, Prague, 2019
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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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Cluster Management System
Distribution Systems Project
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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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