Follow
Chirag Agarwal
Chirag Agarwal
Postdoctoral Research Fellow, Harvard
Verified email at hbs.edu - Homepage
Title
Cited by
Cited by
Year
Towards a unified framework for fair and stable graph representation learning
C Agarwal, H Lakkaraju, M Zitnik
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021
1872021
Openxai: Towards a transparent evaluation of model explanations
C Agarwal, S Krishna, E Saxena, M Pawelczyk, N Johnson, I Puri, M Zitnik, ...
Advances in neural information processing systems 35, 15784-15799, 2022
1242022
Coronet: a deep network architecture for enhanced identification of covid-19 from chest x-ray images
C Agarwal, S Khobahi, D Schonfeld, M Soltanalian
Medical Imaging 2021: Computer-Aided Diagnosis 11597, 484-490, 2021
100*2021
Estimating example difficulty using variance of gradients
C Agarwal, D D'souza, S Hooker
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
932022
Sam: The sensitivity of attribution methods to hyperparameters
N Bansal, C Agarwal, A Nguyen
Proceedings of the ieee/cvf conference on computer vision and pattern …, 2020
842020
Evaluating explainability for graph neural networks
C Agarwal, O Queen, H Lakkaraju, M Zitnik
Scientific Data 10 (1), 144, 2023
772023
Explaining image classifiers by removing input features using generative models
C Agarwal, A Nguyen
Proceedings of the Asian Conference on Computer Vision, 2020
72*2020
Certifying llm safety against adversarial prompting
A Kumar, C Agarwal, S Srinivas, S Feizi, H Lakkaraju
arXiv preprint arXiv:2309.02705, 2023
682023
Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis
M Pawelczyk, C Agarwal, S Joshi, S Upadhyay, H Lakkaraju
International Conference on Artificial Intelligence and Statistics, 4574-4594, 2022
68*2022
Probing gnn explainers: A rigorous theoretical and empirical analysis of gnn explanation methods
C Agarwal, M Zitnik, H Lakkaraju
International Conference on Artificial Intelligence and Statistics, 8969-8996, 2022
64*2022
Towards the unification and robustness of perturbation and gradient based explanations
S Agarwal, S Jabbari, C Agarwal, S Upadhyay, S Wu, H Lakkaraju
International Conference on Machine Learning, 110-119, 2021
602021
Accurate segmentation of lung fields on chest radiographs using deep convolutional networks
MR Arbabshirani, AH Dallal, C Agarwal, A Patel, G Moore
SPIE Medical Imaging, 2017
512017
Rethinking Stability for Attribution-based Explanations
C Agarwal, N Johnson, M Pawelczyk, S Krishna, E Saxena, M Zitnik, ...
arXiv preprint arXiv:2203.06877, 2022
422022
Dear: Debiasing vision-language models with additive residuals
A Seth, M Hemani, C Agarwal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
282023
Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images
AH Dallal, C Agarwal, MR Arbabshirani, A Patel, G Moore
SPIE Medical Imaging, 2017
282017
Gnndelete: A general strategy for unlearning in graph neural networks
J Cheng, G Dasoulas, H He, C Agarwal, M Zitnik
arXiv preprint arXiv:2302.13406, 2023
262023
Improving robustness to adversarial examples by encouraging discriminative features
C Agarwal, A Nguyen, D Schonfeld
2019 IEEE International Conference on Image Processing (ICIP), 3801-3505, 2019
252019
Statistical sequential analysis for object-based video forgery detection
M Aloraini, M Sharifzadeh, C Agarwal, D Schonfeld
Electronic Imaging 31, 1-7, 2019
192019
A tale of two long tails
D D'souza, Z Nussbaum, C Agarwal, S Hooker
arXiv preprint arXiv:2107.13098, 2021
182021
Deep-url: A model-aware approach to blind deconvolution based on deep unfolded richardson-lucy network
C Agarwal, S Khobahi, A Bose, M Soltanalian, D Schonfeld
2020 IEEE International Conference on Image Processing (ICIP), 3299-3303, 2020
182020
The system can't perform the operation now. Try again later.
Articles 1–20