Chirag Agarwal

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Himabindu LakkarajuAssistant Professor, Harvard UniversityAdresse e-mail validée de seas.harvard.edu
Marinka ZitnikAssistant Professor, Harvard UniversityAdresse e-mail validée de hms.harvard.edu
Dan SchonfeldProfessor of Electrical and Computer Engineering, Computer Science, and Bioengineering, UniversityAdresse e-mail validée de uic.edu
Anh Totti NguyenAssociate Professor, Auburn UniversityAdresse e-mail validée de auburn.edu
Satyapriya KrishnaHarvard UniversityAdresse e-mail validée de g.harvard.edu
Martin PawelczykPostdoc, Harvard UniversityAdresse e-mail validée de uni-tuebingen.de
Mohammad Reza Arbabshirani, PhDAmazon Web ServicesAdresse e-mail validée de amazon.com
Sara HookerHead of Cohere For AIAdresse e-mail validée de cohere.com
Aounon KumarResearch Associate, Harvard UniversityAdresse e-mail validée de hbs.edu
Mojtaba SoltanalianAssociate Professor of Electrical and Computer Engineering, UICAdresse e-mail validée de uic.edu
Gregory J. Moore MD, PhDMicrosoft Corporate Vice President, Health and Life SciencesAdresse e-mail validée de microsoft.com
Arindam BosePhD, Senior Research Engineer, KMB TelematicsAdresse e-mail validée de uic.edu
Shalmali JoshiColumbia UniversityAdresse e-mail validée de cumc.columbia.edu
Suraj SrinivasResearch Scientist at BoschAdresse e-mail validée de us.bosch.com
Soheil FeiziUniversity of Maryland, College ParkAdresse e-mail validée de cs.umd.edu
Shahin JabbariDrexel UniversityAdresse e-mail validée de drexel.edu
Zhiwei Steven WuCarnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Nan JiangAssociate Professor of Computer Science, UIUCAdresse e-mail validée de illinois.edu
Babak Mokhlesi, MD, M.Sc.The J. Bailey Carter MD Professor and Division Chief of Pulmonary/Critical Care/Sleep Medicine, RushAdresse e-mail validée de rush.edu
Vineeth N BalasubramanianProfessor, Indian Institute of Technology, Hyderabad, IndiaAdresse e-mail validée de iith.ac.in

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Chirag Agarwal

Assistant Professor, UVA

Adresse e-mail validée de virginia.edu - Page d'accueil

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Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	214	2021
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	152	2022
Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Scientific Data 10 (1), 144, 2023	115	2023
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023	114	2023
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	109	2022
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	104*	2021
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	97	2020
Explaining image classifiers by removing input features using generative models C Agarwal, A Nguyen Proceedings of the Asian Conference on Computer Vision, 2020	78*	2020
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	72*	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	71*	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	71	2021
Rethinking Stability for Attribution-based Explanations C Agarwal, N Johnson, M Pawelczyk, S Krishna, E Saxena, M Zitnik, ... arXiv preprint arXiv:2203.06877, 2022	52	2022
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	51	2017
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	49	2023
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	42	2023
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	32	2017
Are Large Language Models Post Hoc Explainers? N Kroeger, D Ley, S Krishna, C Agarwal, H Lakkaraju arXiv preprint arXiv:2310.05797, 2023	29	2023
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	27	2019
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	20	2020
Statistical sequential analysis for object-based video forgery detection M Aloraini, M Sharifzadeh, C Agarwal, D Schonfeld Electronic Imaging 31, 1-7, 2019	20	2019

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