| 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 | 227 | 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 | 170 | 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 | 142 | 2023 |
| Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Scientific Data 10 (1), 144, 2023 | 138 | 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 | 120 | 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 | 105* | 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 | 101 | 2020 |
| Explaining image classifiers by removing input features using generative models C Agarwal, A Nguyen Proceedings of the Asian Conference on Computer Vision, 2020 | 81* | 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 | 79* | 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 | 78* | 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 | 76 | 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 | 57 | 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 | 52 | 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 | 51 | 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 |
| 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 arXiv preprint arXiv:2310.05797, 2023 | 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 |
| 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 | 26 | 2024 |
| 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 | 25 | 2024 |
Himabindu LakkarajuAssistant Professor, Harvard University確認したメール アドレス: seas.harvard.edu