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2020 – today
- 2025
[i38]Tarun Ram Menta, Susmit Agrawal, Chirag Agarwal:
Analyzing Memorization in Large Language Models through the Lens of Model Attribution. CoRR abs/2501.05078 (2025)- [i37]Akash Ghosh, Debayan Datta, Sriparna Saha, Chirag Agarwal:
The Multilingual Mind : A Survey of Multilingual Reasoning in Language Models. CoRR abs/2502.09457 (2025) - 2024
- [c30]Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju:
On the Trade-offs between Adversarial Robustness and Actionable Explanations. AIES (1) 2024: 784-795 - [c29]Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju:
Quantifying Uncertainty in Natural Language Explanations of Large Language Models. AISTATS 2024: 1072-1080 - [c28]Tarun Ram Menta, Surgan Jandial, Akash Patil, Saketh Bachu, Vimal K. B., Balaji Krishnamurthy, Vineeth N. Balasubramanian, Mausoom Sarkar, Chirag Agarwal:
Active Transferability Estimation. CVPR Workshops 2024: 2659-2670 - [c27]Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju:
Understanding the Effects of Iterative Prompting on Truthfulness. ICML 2024 - [c26]Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju:
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. NeurIPS 2024 - [i36]Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju:
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models. CoRR abs/2402.04614 (2024) - [i35]Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju:
Understanding the Effects of Iterative Prompting on Truthfulness. CoRR abs/2402.06625 (2024) - [i34]Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju:
Towards Safe and Aligned Large Language Models for Medicine. CoRR abs/2403.03744 (2024) - [i33]Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, Himabindu Lakkaraju:
On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. CoRR abs/2406.10625 (2024) - [i32]Abhinav Java, Simra Shahid, Chirag Agarwal:
Towards Operationalizing Right to Data Protection. CoRR abs/2411.08506 (2024) - [i31]Elita A. Lobo, Chirag Agarwal, Himabindu Lakkaraju:
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning. CoRR abs/2411.15382 (2024) - [i30]Ashish Seth, Dinesh Manocha, Chirag Agarwal:
HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models. CoRR abs/2412.20622 (2024) - 2023
- [c25]Ashish Seth, Mayur Hemani, Chirag Agarwal:
DeAR: Debiasing Vision-Language Models with Additive Residuals. CVPR 2023: 6820-6829 - [c24]Jiali Cheng, George Dasoulas, Huan He, Chirag Agarwal, Marinka Zitnik:
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks. ICLR 2023 - [c23]Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian:
Explaining RL Decisions with Trajectories. ICLR 2023 - [c22]Michael Llordes
, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal:
Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation. SIGIR 2023: 1976-1980 - [i29]Tarun Ram Menta, Surgan Jandial, Akash Patil, Vimal KB, Saketh Bachu, Balaji Krishnamurthy, Vineeth N. Balasubramanian, Chirag Agarwal, Mausoom Sarkar:
Towards Estimating Transferability using Hard Subsets. CoRR abs/2301.06928 (2023) - [i28]Jiali Cheng, George Dasoulas, Huan He, Chirag Agarwal, Marinka Zitnik:
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks. CoRR abs/2302.13406 (2023) - [i27]Ashish Seth, Mayur Hemani, Chirag Agarwal:
DeAR: Debiasing Vision-Language Models with Additive Residuals. CoRR abs/2303.10431 (2023) - [i26]Michael Llordes, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal:
Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation. CoRR abs/2304.12631 (2023) - [i25]Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian:
Explaining RL Decisions with Trajectories. CoRR abs/2305.04073 (2023) - [i24]Shripad Vilasrao Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian, Chirag Agarwal:
Counterfactual Explanation Policies in RL. CoRR abs/2307.13192 (2023) - [i23]Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju:
Certifying LLM Safety against Adversarial Prompting. CoRR abs/2309.02705 (2023) - [i22]Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju:
On the Trade-offs between Adversarial Robustness and Actionable Explanations. CoRR abs/2309.16452 (2023) - [i21]Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju:
Are Large Language Models Post Hoc Explainers? CoRR abs/2310.05797 (2023) - [i20]Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju:
Quantifying Uncertainty in Natural Language Explanations of Large Language Models. CoRR abs/2311.03533 (2023) - 2022
- [c21]Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju:
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS 2022: 4574-4594 - [c20]Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju:
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. AISTATS 2022: 8969-8996 - [c19]Chirag Agarwal, Daniel D'souza, Sara Hooker:
Estimating Example Difficulty using Variance of Gradients. CVPR 2022: 10358-10368 - [c18]Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal:
Towards Training GNNs Using Explanation Directed Message Passing. LoG 2022: 28 - [c17]Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju:
OpenXAI: Towards a Transparent Evaluation of Model Explanations. NeurIPS 2022 - [i19]Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju:
Rethinking Stability for Attribution-based Explanations. CoRR abs/2203.06877 (2022) - [i18]Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju:
OpenXAI: Towards a Transparent Evaluation of Model Explanations. CoRR abs/2206.11104 (2022) - [i17]Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik:
Evaluating Explainability for Graph Neural Networks. CoRR abs/2208.09339 (2022) - [i16]Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal:
Towards Training GNNs using Explanation Directed Message Passing. CoRR abs/2211.16731 (2022) - 2021
- [c16]Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju:
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. ICML 2021: 110-119 - [c15]Chirag Agarwal, Shahin Khobahi, Dan Schonfeld, Mojtaba Soltanalian:
CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images. Computer-Aided Diagnosis 2021 - [c14]Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik:
Towards a unified framework for fair and stable graph representation learning. UAI 2021: 2114-2124 - [i15]Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju:
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR abs/2102.10618 (2021) - [i14]Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik:
Towards a Unified Framework for Fair and Stable Graph Representation Learning. CoRR abs/2102.13186 (2021) - [i13]Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju:
Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations. CoRR abs/2106.09078 (2021) - [i12]Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju:
On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR abs/2106.09992 (2021) - [i11]Daniel D'souza, Zach Nussbaum, Chirag Agarwal, Sara Hooker:
A Tale Of Two Long Tails. CoRR abs/2107.13098 (2021) - 2020
- [c13]Chirag Agarwal, Anh Nguyen:
Explaining Image Classifiers by Removing Input Features Using Generative Models. ACCV (6) 2020: 101-118 - [c12]Naman Bansal, Chirag Agarwal, Anh Nguyen:
SAM: The Sensitivity of Attribution Methods to Hyperparameters. CVPR Workshops 2020: 11-21 - [c11]Naman Bansal, Chirag Agarwal, Anh Nguyen:
SAM: The Sensitivity of Attribution Methods to Hyperparameters. CVPR 2020: 8670-8680 - [c10]Chirag Agarwal, Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian, Dan Schonfeld:
DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network. ICIP 2020: 3299-3303 - [i10]Chirag Agarwal, Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian, Dan Schonfeld:
Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network. CoRR abs/2002.01053 (2020) - [i9]Naman Bansal, Chirag Agarwal, Anh Nguyen:
SAM: The Sensitivity of Attribution Methods to Hyperparameters. CoRR abs/2003.08754 (2020) - [i8]Chirag Agarwal, Peijie Chen, Anh Nguyen:
Intriguing generalization and simplicity of adversarially trained neural networks. CoRR abs/2006.09373 (2020) - [i7]Chirag Agarwal, Sara Hooker:
Estimating Example Difficulty using Variance of Gradients. CoRR abs/2008.11600 (2020)
2010 – 2019
- 2019
- [c9]Chirag Agarwal, Anh Nguyen, Dan Schonfeld:
Improving Robustness to Adversarial Examples by Encouraging Discriminative Features. ICIP 2019: 3801-3505 - [c8]Mohammed Aloraini, Mehdi Sharifzadeh, Chirag Agarwal, Dan Schonfeld:
Statistical Sequential Analysis for Object-based Video Forgery Detection. Media Watermarking, Security, and Forensics 2019 - [i6]Chirag Agarwal, Dan Schonfeld, Anh Nguyen:
Removing input features via a generative model to explain their attributions to classifier's decisions. CoRR abs/1910.04256 (2019) - 2018
- [c7]Nivedita Khobragade, Chirag Agarwal:
Multi-class segmentation of neuronal electron microscopy images using deep learning. Image Processing 2018: 105742W - [c6]Chirag Agarwal, Mehdi Sharifzadeh, Dan Schonfeld:
CrossEncoders: A complex neural network compression framework. Visual Information Processing and Communication 2018 - [i5]Chirag Agarwal, Bo Dong, Dan Schonfeld, Anthony Hoogs:
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks. CoRR abs/1806.01477 (2018) - [i4]Chirag Agarwal, Anh Nguyen, Dan Schonfeld:
Improving Adversarial Robustness by Encouraging Discriminative Features. CoRR abs/1811.00621 (2018) - 2017
- [c5]Ahmed H. Dallal, Chirag Agarwal, Mohammad R. Arbabshirani, Aalpen Patel, Gregory J. Moore:
Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images. Computer-Aided Diagnosis 2017: 101340K - [c4]Chirag Agarwal, Ahmed H. Dallal, Mohammad R. Arbabshirani, Aalpen Patel, Gregory J. Moore:
Unsupervised quantification of abdominal fat from CT images using Greedy Snakes. Image Processing 2017: 101332T - [c3]Mohammad R. Arbabshirani, Ahmed H. Dallal, Chirag Agarwal, Aalpen Patel, Gregory J. Moore:
Accurate segmentation of lung fields on chest radiographs using deep convolutional networks. Image Processing 2017: 1013305 - [c2]Mehdi Sharifzadeh, Chirag Agarwal, Mohammed Aloraini, Dan Schonfeld:
Convolutional neural network steganalysis's application to steganography. VCIP 2017: 1-4 - [i3]Chirag Agarwal, Mehdi Sharifzadeh, Joe Klobusicky, Dan Schonfeld:
CrossNets : A New Approach to Complex Learning. CoRR abs/1705.07404 (2017) - [i2]Mehdi Sharifzadeh, Chirag Agarwal, Mahdi Salarian, Dan Schonfeld:
A New Parallel Message-distribution Technique for Cost-based Steganography. CoRR abs/1705.08616 (2017) - [i1]Mehdi Sharifzadeh, Chirag Agarwal, Mohammed Aloraini, Dan Schonfeld:
Convolutional Neural Network Steganalysis's Application to Steganography. CoRR abs/1711.02581 (2017) - 2015
- [c1]Chirag Agarwal, Paul Hylander, Yogesh Mahajan, Jonathan Michelson, Vigyan Singhal:
Compositional Reasoning Gotchas in Practice. FMCAD 2015: 17-24
Coauthor Index
aka: Hima Lakkaraju
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