Chirag Agarwal

Dikutip oleh

	Semua	Sejak 2020
Kutipan	2062	1997
indeks-h	22	22
indeks-i10	29	26

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

Ikuti

Chirag Agarwal

Assistant Professor, UVA

Email yang diverifikasi di virginia.edu - Beranda

XAI TrustworthyML Artificial Intelligence


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	226	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	188	2022
Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023	165	2023
Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Scientific Data 10 (1), 144, 2023	149	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	118	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	100*	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	99	2020
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	81*	2022
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	81*	2022
Explaining image classifiers by removing input features using generative models C Agarwal, A Nguyen Proceedings of the Asian Conference on Computer Vision, 2020	80*	2020
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	74	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	62	2022
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	54	2023
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	53	2017
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	46	2023
Faithfulness vs. plausibility: On the (un) reliability of explanations from large language models C Agarwal, SH Tanneru, H Lakkaraju arXiv preprint arXiv:2402.04614, 2024	38*	2024
Quantifying uncertainty in natural language explanations of large language models SH Tanneru, C Agarwal, H Lakkaraju International Conference on Artificial Intelligence and Statistics, 1072-1080, 2024	35	2024
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	34	2017
Are large language models post hoc explainers? N Kroeger, D Ley, S Krishna, C Agarwal, H Lakkaraju	30	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	30	2019

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