| On calibration of modern neural networks C Guo, G Pleiss, Y Sun, KQ Weinberger International conference on machine learning, 1321-1330, 2017 | 5756 | 2017 |
| Countering adversarial images using input transformations C Guo, M Rana, M Cisse, L Van Der Maaten arXiv preprint arXiv:1711.00117, 2017 | 1569 | 2017 |
| Simple black-box adversarial attacks C Guo, J Gardner, Y You, AG Wilson, K Weinberger International conference on machine learning, 2484-2493, 2019 | 576 | 2019 |
| An empirical study on evaluation metrics of generative adversarial networks Q Xu, G Huang, Y Yuan, C Guo, Y Sun, F Wu, K Weinberger arXiv preprint arXiv:1806.07755, 2018 | 358 | 2018 |
| React: Out-of-distribution detection with rectified activations Y Sun, C Guo, Y Li Advances in Neural Information Processing Systems 34, 144-157, 2021 | 351 | 2021 |
| Certified data removal from machine learning models C Guo, T Goldstein, A Hannun, L Van Der Maaten arXiv preprint arXiv:1911.03030, 2019 | 338 | 2019 |
| Supervised word mover's distance G Huang, C Guo, MJ Kusner, Y Sun, F Sha, KQ Weinberger Advances in neural information processing systems 29, 2016 | 230 | 2016 |
| Low frequency adversarial perturbation C Guo, JS Frank, KQ Weinberger arXiv preprint arXiv:1809.08758, 2018 | 170 | 2018 |
| Gradient-based adversarial attacks against text transformers C Guo, A Sablayrolles, H Jégou, D Kiela arXiv preprint arXiv:2104.13733, 2021 | 138 | 2021 |
| A new defense against adversarial images: Turning a weakness into a strength S Hu, T Yu, C Guo, WL Chao, KQ Weinberger Advances in neural information processing systems 32, 2019 | 124 | 2019 |
| Discovering and exploiting additive structure for Bayesian optimization J Gardner, C Guo, K Weinberger, R Garnett, R Grosse Artificial Intelligence and Statistics, 1311-1319, 2017 | 119 | 2017 |
| On the importance of difficulty calibration in membership inference attacks L Watson, C Guo, G Cormode, A Sablayrolles arXiv preprint arXiv:2111.08440, 2021 | 83 | 2021 |
| Breaking the glass ceiling for embedding-based classifiers for large output spaces C Guo, A Mousavi, X Wu, DN Holtmann-Rice, S Kale, S Reddi, S Kumar Advances in Neural Information Processing Systems 32, 2019 | 66 | 2019 |
| Online adaptation to label distribution shift R Wu, C Guo, Y Su, KQ Weinberger Advances in Neural Information Processing Systems 34, 11340-11351, 2021 | 51 | 2021 |
| Eiffel: Ensuring integrity for federated learning A Roy Chowdhury, C Guo, S Jha, L van der Maaten Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022 | 50 | 2022 |
| Measuring data leakage in machine-learning models with fisher information A Hannun, C Guo, L van der Maaten Uncertainty in Artificial Intelligence, 760-770, 2021 | 49 | 2021 |
| Bounding training data reconstruction in private (deep) learning C Guo, B Karrer, K Chaudhuri, L van der Maaten International Conference on Machine Learning, 8056-8071, 2022 | 46 | 2022 |
| On Hiding Neural Networks Inside Neural Networks C Guo, R Wu, KQ Weinberger arXiv preprint arXiv:2002.10078, 2020 | 33* | 2020 |
| Palindromic rich words and run-length encodings C Guo, J Shallit, AM Shur Information Processing Letters 116 (12), 735-738, 2016 | 29* | 2016 |
| Making paper reviewing robust to bid manipulation attacks R Wu, C Guo, F Wu, R Kidambi, L Van Der Maaten, K Weinberger International Conference on Machine Learning, 11240-11250, 2021 | 27 | 2021 |
Kilian WeinbergerCornell University在 cornell.edu 的电子邮件经过验证
