Theo dõi
Chirag Agarwal
Chirag Agarwal
Assistant Professor, UVA
Email được xác minh tại virginia.edu - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
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
2262021
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
1882022
Certifying llm safety against adversarial prompting
A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju
arXiv preprint arXiv:2309.02705, 2023
1652023
Evaluating explainability for graph neural networks
C Agarwal, O Queen, H Lakkaraju, M Zitnik
Scientific Data 10 (1), 144, 2023
1492023
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
1182022
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
992020
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
742021
Rethinking Stability for Attribution-based Explanations
C Agarwal, N Johnson, M Pawelczyk, S Krishna, E Saxena, M Zitnik, ...
arXiv preprint arXiv:2203.06877, 2022
622022
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
542023
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
532017
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
462023
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
352024
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
342017
Are large language models post hoc explainers?
N Kroeger, D Ley, S Krishna, C Agarwal, H Lakkaraju
302023
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
302019
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