| ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining J Chen, Y Li, X Wu, Y Liang, S Jha Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 111 | 2021 |
| Robust Out-of-distribution Detection for Neural Networks J Chen, Y Li, X Wu, Y Liang, S Jha arXiv preprint arXiv:2003.09711, 2020 | 77 | 2020 |
| Robust Attribution Regularization J Chen, X Wu, V Rastogi, Y Liang, S Jha arXiv preprint arXiv:1905.09957, 2019 | 77 | 2019 |
| Concise Explanations of Neural Networks using Adversarial Training P Chalasani, J Chen, AR Chowdhury, S Jha, X Wu International Conference on Machine Learning, 2020 | 63 | 2020 |
| Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles J Chen, F Liu, B Avci, X Wu, Y Liang, S Jha Advances in Neural Information Processing Systems 34, 2021 | 56 | 2021 |
| AI-GAN: Attack-inspired generation of adversarial examples T Bai, J Zhao, J Zhu, S Han, J Chen, B Li, A Kot 2021 IEEE International Conference on Image Processing (ICIP), 2543-2547, 2021 | 52 | 2021 |
| Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks J Chen, X Wu, V Rastogi, Y Liang, S Jha arXiv preprint arXiv:1805.07816, 2018 | 39* | 2018 |
| Reinforcing adversarial robustness using model confidence induced by adversarial training X Wu, U Jang, J Chen, L Chen, S Jha International Conference on Machine Learning, 5334-5342, 2018 | 27 | 2018 |
| The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning Z Shi, J Chen, K Li, J Raghuram, X Wu, Y Liang, S Jha arXiv preprint arXiv:2303.00106, 2023 | 15 | 2023 |
| Towards Evaluating the Robustness of Neural Networks Learned by Transduction J Chen, X Wu, Y Guo, Y Liang, S Jha arXiv preprint arXiv:2110.14735, 2021 | 15 | 2021 |
| ReabsNet: Detecting and Revising Adversarial Examples J Chen, Z Meng, C Sun, W Tang, Y Zhu arXiv preprint arXiv:1712.08250, 2017 | 13 | 2017 |
| GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems R Feng, N Mangaokar, J Chen, E Fernandes, S Jha, A Prakash 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 664-683, 2022 | 11 | 2022 |
| Query-efficient physical hard-label attacks on deep learning visual classification R Feng, J Chen, N Manohar, E Fernandes, S Jha, A Prakash arXiv preprint arXiv:2002.07088, 2020 | 10 | 2020 |
| Robust out-of-distribution detection via informative outlier mining J Chen, Y Li, X Wu, Y Liang, S Jha arXiv preprint arXiv:2006.15207 1 (2), 7, 2020 | 10 | 2020 |
| Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation X Wu, Y Guo, J Chen, Y Liang, S Jha, P Chalasani arXiv preprint arXiv:2004.10390, 2020 | 9 | 2020 |
| Is forgetting less a good inductive bias for forward transfer? J Chen, T Nguyen, D Gorur, A Chaudhry arXiv preprint arXiv:2303.08207, 2023 | 8 | 2023 |
| Revisiting adversarial robustness of classifiers with a reject option J Chen, J Raghuram, J Choi, X Wu, Y Liang, S Jha The AAAI-22 Workshop on Adversarial Machine Learning and Beyond, 2021 | 8 | 2021 |
| Concept-based explanations for out-of-distribution detectors J Choi, J Raghuram, R Feng, J Chen, S Jha, A Prakash International Conference on Machine Learning, 5817-5837, 2023 | 7 | 2023 |
| Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-Based Method T Bai, J Zhao, J Zhu, S Han, J Chen, B Li, A Kot IEEE Internet of Things Journal 9 (3), 1875-1884, 2021 | 7 | 2021 |
| Towards Adversarial Robustness via Transductive Learning J Chen, Y Guo, X Wu, T Li, Q Lao, Y Liang, S Jha arXiv preprint arXiv:2106.08387, 2021 | 5 | 2021 |
Somesh JhaLubar Chair of Computer Science, University of WisconsinEmail verificata su cs.wisc.edu
