Chirag Agarwal

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	Visi	Nuo 2020
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Himabindu LakkarajuAssistant Professor, Harvard UniversityPatvirtintas el. paštas seas.harvard.edu
Marinka ZitnikAssociate Professor, Harvard UniversityPatvirtintas el. paštas hms.harvard.edu
Dan SchonfeldProfessor of Electrical and Computer Engineering, Computer Science, and Bioengineering, UniversityPatvirtintas el. paštas uic.edu
Anh Totti NguyenAssociate Professor, Auburn UniversityPatvirtintas el. paštas auburn.edu
Satyapriya KrishnaHarvard UniversityPatvirtintas el. paštas g.harvard.edu
Martin PawelczykPostdoc, Harvard UniversityPatvirtintas el. paštas uni-tuebingen.de
Aounon KumarResearch Associate, Harvard UniversityPatvirtintas el. paštas hbs.edu
Sara HookerHead of Cohere For AIPatvirtintas el. paštas cohere.com
Mohammad Reza Arbabshirani, PhDAmazon Web ServicesPatvirtintas el. paštas amazon.com
Mojtaba SoltanalianAssociate Professor of Electrical and Computer Engineering, UICPatvirtintas el. paštas uic.edu
Gregory J. Moore MD, PhDMicrosoft Corporate Vice President, Health and Life SciencesPatvirtintas el. paštas microsoft.com
Arindam BosePhD, Senior Research Engineer, KMB TelematicsPatvirtintas el. paštas uic.edu
Shalmali JoshiColumbia UniversityPatvirtintas el. paštas cumc.columbia.edu
Suraj SrinivasResearch Scientist at BoschPatvirtintas el. paštas us.bosch.com
Soheil FeiziUniversity of Maryland, College ParkPatvirtintas el. paštas cs.umd.edu
Shahin JabbariDrexel UniversityPatvirtintas el. paštas drexel.edu
Zhiwei Steven WuCarnegie Mellon UniversityPatvirtintas el. paštas andrew.cmu.edu
Nan JiangAssociate Professor of Computer Science, UIUCPatvirtintas el. paštas illinois.edu
Vineeth N BalasubramanianProfessor, Indian Institute of Technology, Hyderabad, IndiaPatvirtintas el. paštas iith.ac.in
Babak Mokhlesi, MD, M.Sc.The J. Bailey Carter MD Professor and Division Chief of Pulmonary/Critical Care/Sleep Medicine, RushPatvirtintas el. paštas rush.edu

Stebėti

Chirag Agarwal

Assistant Professor, UVA

Patvirtintas el. paštas virginia.edu - Pagrindinis puslapis

XAI TrustworthyML Artificial Intelligence


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	185	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	160	2023
Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Scientific Data 10 (1), 144, 2023	144	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
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
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	79*	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	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	61	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	45	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	36*	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
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	33	2024
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
Are large language models post hoc explainers? N Kroeger, D Ley, S Krishna, C Agarwal, H Lakkaraju	29	2023

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