Compositional Reasoning Gotchas in Practice. - Chirag Agarwal; Paul Hylander; Yogesh Mahajan; Jonathan Michelson; Vigyan Singhal (FMCAD, 2015)

Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images. - Ahmed H. Dallal; Chirag Agarwal; Mohammad R. Arbabshirani; Aalpen Patel; Gregory J. Moore (Computer-Aided Diagnosis, 2017)

Unsupervised quantification of abdominal fat from CT images using Greedy Snakes. - Chirag Agarwal; Ahmed H. Dallal; Mohammad R. Arbabshirani; Aalpen Patel; Gregory J. Moore (Image Processing, 2017)

Accurate segmentation of lung fields on chest radiographs using deep convolutional networks. - Mohammad R. Arbabshirani; Ahmed H. Dallal; Chirag Agarwal; Aalpen Patel; Gregory J. Moore (Image Processing, 2017)

Convolutional neural network steganalysis's application to steganography. - Mehdi Sharifzadeh; Chirag Agarwal; Mohammed Aloraini; Dan Schonfeld (VCIP, 2017)

CrossNets : A New Approach to Complex Learning. - Chirag Agarwal; Mehdi Sharifzadeh; Joe Klobusicky; Dan Schonfeld (CoRR, 2017)

A New Parallel Message-distribution Technique for Cost-based Steganography. - Mehdi Sharifzadeh; Chirag Agarwal; Mahdi Salarian; Dan Schonfeld (CoRR, 2017)

Multi-class segmentation of neuronal electron microscopy images using deep learning. - Nivedita Khobragade; Chirag Agarwal (Image Processing, 2018)

CrossEncoders: A complex neural network compression framework. - Chirag Agarwal; Mehdi Sharifzadeh; Dan Schonfeld (Visual Information Processing and Communication, 2018)

An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks. - Chirag Agarwal; Bo Dong; Dan Schonfeld; Anthony Hoogs (CoRR, 2018)

Improving Adversarial Robustness by Encouraging Discriminative Features. - Chirag Agarwal; Anh Nguyen 002; Dan Schonfeld (CoRR, 2018)

Improving Robustness to Adversarial Examples by Encouraging Discriminative Features. - Chirag Agarwal; Anh Nguyen 002; Dan Schonfeld (ICIP, 2019)

Statistical Sequential Analysis for Object-based Video Forgery Detection. - Mohammed Aloraini; Mehdi Sharifzadeh; Chirag Agarwal; Dan Schonfeld (Media Watermarking, Security, and Forensics, 2019)

Removing input features via a generative model to explain their attributions to classifier's decisions. - Chirag Agarwal; Dan Schonfeld; Anh Nguyen 002 (CoRR, 2019)

Explaining Image Classifiers by Removing Input Features Using Generative Models. - Chirag Agarwal; Anh Nguyen 002 (ACCV (6), 2020)

SAM: The Sensitivity of Attribution Methods to Hyperparameters. - Naman Bansal; Chirag Agarwal; Anh Nguyen 002 (CVPR Workshops, 2020)

DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network. - Chirag Agarwal; Shahin Khobahi; Arindam Bose; Mojtaba Soltanalian; Dan Schonfeld (ICIP, 2020)

Intriguing generalization and simplicity of adversarially trained neural networks. - Chirag Agarwal; Peijie Chen; Anh Nguyen 002 (CoRR, 2020)

Estimating Example Difficulty using Variance of Gradients. - Chirag Agarwal; Sara Hooker (CoRR, 2020)

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. - Sushant Agarwal; Shahin Jabbari; Chirag Agarwal; Sohini Upadhyay; Steven Wu 001; Himabindu Lakkaraju (ICML, 2021)

CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images. - Chirag Agarwal; Shahin Khobahi; Dan Schonfeld; Mojtaba Soltanalian (Computer-Aided Diagnosis, 2021)

Towards a unified framework for fair and stable graph representation learning. - Chirag Agarwal; Himabindu Lakkaraju; Marinka Zitnik (UAI, 2021)

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations. - Chirag Agarwal; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2021)

On the Connections between Counterfactual Explanations and Adversarial Examples. - Martin Pawelczyk; Shalmali Joshi; Chirag Agarwal; Sohini Upadhyay; Himabindu Lakkaraju (CoRR, 2021)

A Tale Of Two Long Tails. - Daniel D'souza; Zach Nussbaum; Chirag Agarwal; Sara Hooker (CoRR, 2021)

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. - Chirag Agarwal; Marinka Zitnik; Himabindu Lakkaraju (AISTATS, 2022)

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. - Martin Pawelczyk; Chirag Agarwal; Shalmali Joshi; Sohini Upadhyay; Himabindu Lakkaraju (AISTATS, 2022)

Estimating Example Difficulty using Variance of Gradients. - Chirag Agarwal; Daniel D'souza; Sara Hooker (CVPR, 2022)

Towards Training GNNs Using Explanation Directed Message Passing. - Valentina Giunchiglia; Chirag Varun Shukla; Guadalupe Gonzalez; Chirag Agarwal (LoG, 2022)

OpenXAI: Towards a Transparent Evaluation of Model Explanations. - Chirag Agarwal; Satyapriya Krishna; Eshika Saxena; Martin Pawelczyk; Nari Johnson; Isha Puri; Marinka Zitnik; Himabindu Lakkaraju (NeurIPS, 2022)

Rethinking Stability for Attribution-based Explanations. - Chirag Agarwal; Nari Johnson; Martin Pawelczyk; Satyapriya Krishna; Eshika Saxena; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2022)

Evaluating Explainability for Graph Neural Networks. - Chirag Agarwal; Owen Queen; Himabindu Lakkaraju; Marinka Zitnik (CoRR, 2022)

DeAR: Debiasing Vision-Language Models with Additive Residuals. - Ashish Seth; Mayur Hemani; Chirag Agarwal (CVPR, 2023)

GNNDelete: A General Strategy for Unlearning in Graph Neural Networks. - Jiali Cheng; George Dasoulas; Huan He; Chirag Agarwal; Marinka Zitnik (ICLR, 2023)

Explaining RL Decisions with Trajectories. - Shripad Vilasrao Deshmukh; Arpan Dasgupta; Balaji Krishnamurthy; Nan Jiang; Chirag Agarwal; Georgios Theocharous; Jayakumar Subramanian (ICLR, 2023)

Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation. - Michael Llordes; Debasis Ganguly; Sumit Bhatia; Chirag Agarwal (SIGIR, 2023)

Towards Estimating Transferability using Hard Subsets. - Tarun Ram Menta; Surgan Jandial; Akash Patil; Vimal KB; Saketh Bachu; Balaji Krishnamurthy; Vineeth N. Balasubramanian; Chirag Agarwal; Mausoom Sarkar (CoRR, 2023)

Counterfactual Explanation Policies in RL. - Shripad Vilasrao Deshmukh; Srivatsan R; Supriti Vijay; Jayakumar Subramanian; Chirag Agarwal (CoRR, 2023)

Certifying LLM Safety against Adversarial Prompting. - Aounon Kumar; Chirag Agarwal; Suraj Srinivas; Soheil Feizi; Hima Lakkaraju (CoRR, 2023)

Are Large Language Models Post Hoc Explainers? - Nicholas Kroeger; Dan Ley; Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2023)

On the Trade-offs between Adversarial Robustness and Actionable Explanations. - Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (AIES (1), 2024)

Quantifying Uncertainty in Natural Language Explanations of Large Language Models. - Sree Harsha Tanneru; Chirag Agarwal; Himabindu Lakkaraju (AISTATS, 2024)

Active Transferability Estimation. - Tarun Ram Menta; Surgan Jandial; Akash Patil; Saketh Bachu; Vimal K. B.; Balaji Krishnamurthy; Vineeth N. Balasubramanian; Mausoom Sarkar; Chirag Agarwal (CVPR Workshops, 2024)

Understanding the Effects of Iterative Prompting on Truthfulness. - Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (ICML, 2024)

MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (NeurIPS, 2024)

Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models. - Chirag Agarwal; Sree Harsha Tanneru; Himabindu Lakkaraju (CoRR, 2024)

Towards Safe and Aligned Large Language Models for Medicine. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. - Sree Harsha Tanneru; Dan Ley; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models. - Ashish Seth; Dinesh Manocha; Chirag Agarwal (CoRR, 2024)

Towards Operationalizing Right to Data Protection. - Abhinav Java; Simra Shahid; Chirag Agarwal (NAACL (Long Papers), 2025)

On the Impact of Fine-Tuning on Chain-of-Thought Reasoning. - Elita A. Lobo; Chirag Agarwal; Himabindu Lakkaraju (NAACL (Long Papers), 2025)

Analyzing Memorization in Large Language Models through the Lens of Model Attribution. - Tarun Ram Menta; Susmit Agrawal; Chirag Agarwal (NAACL (Long Papers), 2025)

The Multilingual Mind : A Survey of Multilingual Reasoning in Language Models. - Akash Ghosh; Debayan Datta; Sriparna Saha 001; Chirag Agarwal (CoRR, 2025)

Rethinking Explainability in the Era of Multimodal AI. - Chirag Agarwal (CoRR, 2025)