| Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks N Papernot, P McDaniel, X Wu, S Jha, A Swami Security and Privacy (SP), 2016 IEEE Symposium on, 582-597, 2016 | 3786 | 2016 |
| Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics X Wu, F Li, A Kumar, K Chaudhuri, S Jha, JF Naughton Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 284 | 2017 |
| A Methodology for Modeling Model-Inversion Attacks X Wu, M Fredrikson, S Jha, JF Naughton Computer Security Foundations Symposium (CSF), 2016 IEEE 29th, 355-370, 2016 | 197* | 2016 |
| Objective metrics and gradient descent algorithms for adversarial examples in machine learning U Jang, X Wu, S Jha Proceedings of the 33rd Annual Computer Security Applications Conference …, 2017 | 143 | 2017 |
| Atom: Robustifying out-of-distribution detection using outlier mining J Chen, Y Li, X Wu, Y Liang, S Jha Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 133 | 2021 |
| Concise Explanations for Neural Networks using Adversarial Training P Chalasani, J Chen, S Jha, X Wu arXiv preprint arXiv:1810.06583, 2018 | 98* | 2018 |
| COREMU: a Scalable and Portable Parallel Full-System Emulator Z Wang, R Liu, Y Chen, X Wu, H Chen, W Zhang, B Zang ACM SIGPLAN Notices 46 (8), 213-222, 2011 | 89 | 2011 |
| Robust attribution regularization J Chen, X Wu, V Rastogi, Y Liang, S Jha Advances in Neural Information Processing Systems 32, 2019 | 87 | 2019 |
| Robust out-of-distribution detection for neural networks J Chen, Y Li, X Wu, Y Liang, S Jha arXiv preprint arXiv:2003.09711, 2020 | 86 | 2020 |
| Weak Compositions and Their Applications to Polynomial Lower Bounds for Kernelization D Hermelin, X Wu Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete …, 2012 | 83 | 2012 |
| A Completeness Theory for Polynomial (Turing) Kernelization D Hermelin, S Kratsch, K Sołtys, M Wahlström, X Wu Algorithmica 71 (3), 702-730, 2015 | 77 | 2015 |
| From Speculation to Security: Practical and Efficient Information Flow Tracking using Speculative Hardware H Chen, X Wu, L Yuan, B Zang, P Yew, FT Chong Computer Architecture, 2008. ISCA'08. 35th International Symposium on, 401-412, 2008 | 66 | 2008 |
| 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, 14980-14992, 2021 | 64 | 2021 |
| Diff: a relational interface for large-scale data explanation F Abuzaid, P Kraft, S Suri, E Gan, E Xu, A Shenoy, A Ananthanarayan, ... Proceedings of the VLDB Endowment 12 (4), 419-432, 2018 | 56 | 2018 |
| Control Flow Obfuscation with Information Flow Tracking H Chen, L Yuan, X Wu, B Zang, B Huang, P Yew Proceedings of the 42nd Annual IEEE/ACM International Symposium on …, 2009 | 55 | 2009 |
| Uncertainty Aware Query Execution Time Prediction W Wu, X Wu, H Hacigümüş, JF Naughton Proceedings of the VLDB Endowment 7 (14), 1857-1868, 2014 | 50 | 2014 |
| Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks J Chen, X Wu, Y Liang, S Jha arXiv preprint arXiv:1805.07816, 2018 | 42* | 2018 |
| Tuple-oriented compression for large-scale mini-batch stochastic gradient descent F Li, L Chen, Y Zeng, A Kumar, X Wu, JF Naughton, JM Patel Proceedings of the 2019 International Conference on Management of Data, 1517 …, 2019 | 38* | 2019 |
| Revisiting Differentially Private Regression: Lessons from Learning Theory and Their Consequences X Wu, M Fredrikson, W Wu, S Jha, JF Naughton arXiv preprint arXiv:1512.06388, 2015 | 33 | 2015 |
| Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training X Wu, U Jang, J Chen, L Chen, S Jha Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 32* | 2018 |
Somesh JhaLubar Chair of Computer Science, University of WisconsinVerified email at cs.wisc.edu
