| Adversarial machine learning L Huang, AD Joseph, B Nelson, BIP Rubinstein, JD Tygar Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence …, 2011 | 1789 | 2011 |
| Exploiting Machine Learning to Subvert Your Spam Filter. B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, ... First USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET'08 …, 2008 | 540 | 2008 |
| ANTIDOTE: Understanding and defending against poisoning of anomaly detectors BIP Rubinstein, B Nelson, L Huang, AD Joseph, S Lau, S Rao, N Taft, ... Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement …, 2009 | 509 | 2009 |
| A Bayesian approach to discovering truth from conflicting sources for data integration B Zhao, BIP Rubinstein, J Gemmell, J Han Proceedings of the VLDB Endowment 5 (6), 550-561, 2012 | 495 | 2012 |
| Learning in a large function space: Privacy-preserving mechanisms for SVM learning BIP Rubinstein, PL Bartlett, L Huang, N Taft Journal of Privacy and Confidentiality 4 (1), 65-100, 2012 | 356 | 2012 |
| Identifying At-Risk Students in Massive Open Online Courses J He, J Bailey, BIP Rubinstein, R Zhang Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI …, 2015 | 291 | 2015 |
| PPFA: Privacy preserving fog-enabled aggregation in smart grid L Lyu, K Nandakumar, B Rubinstein, J Jin, J Bedo, M Palaniswami IEEE Transactions on Industrial Informatics 14 (8), 3733-3744, 2018 | 280 | 2018 |
| Link prediction by de-anonymization: How we won the kaggle social network challenge A Narayanan, E Shi, BIP Rubinstein The 2011 International Joint Conference on Neural Networks (IJCNN), 1825-1834, 2011 | 232 | 2011 |
| Machine learning in network anomaly detection: A survey S Wang, JF Balarezo, S Kandeepan, A Al-Hourani, KG Chavez, ... IEEe Access 9, 152379-152396, 2021 | 226 | 2021 |
| Machine-learning algorithms predict graft failure after liver transplantation L Lau, Y Kankanige, B Rubinstein, R Jones, C Christophi, V Muralidharan, ... Transplantation 101 (4), e125-e132, 2017 | 189 | 2017 |
| Misleading learners: Co-opting your spam filter B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, ... Machine Learning in Cyber Trust, 17-51, 2009 | 161 | 2009 |
| Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors R Zhang, P Madumal, T Miller, K Ehinger, B Rubinstein Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021 | 156* | 2021 |
| Security evaluation of support vector machines in adversarial environments B Biggio, I Corona, B Nelson, BIP Rubinstein, D Maiorca, G Fumera, ... Support vector machines applications, 105-153, 2014 | 146 | 2014 |
| Robust and private Bayesian inference C Dimitrakakis, B Nelson, A Mitrokotsa, BIP Rubinstein International Conference on Algorithmic Learning Theory, 291-305, 2014 | 130 | 2014 |
| Query strategies for evading convex-inducing classifiers B Nelson, BIP Rubinstein, L Huang, AD Joseph, SJ Lee, S Rao, JD Tygar The Journal of Machine Learning Research 13 (1), 1293-1332, 2012 | 125 | 2012 |
| Health data in an open world C Culnane, BIP Rubinstein, V Teague arXiv preprint arXiv:1712.05627, 2017 | 123 | 2017 |
| Adversarial Machine Learning AD Joseph, B Nelson, BIP Rubinstein, JD Tygar Cambridge University Press, 2019 | 120 | 2019 |
| Hard to Forget: Poisoning Attacks on Certified Machine Unlearning NG Marchant, BIP Rubinstein, S Alfeld Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022 | 115 | 2022 |
| Reinforcement learning for autonomous defence in software-defined networking Y Han, BIP Rubinstein, T Abraham, T Alpcan, O De Vel, S Erfani, ... International conference on decision and game theory for security, 145-165, 2018 | 113 | 2018 |
| Adaptive bidding for display advertising A Ghosh, BIP Rubinstein, S Vassilvitskii, M Zinkevich Proceedings of the 18th International Conference on World Wide Web, 251-260, 2009 | 113 | 2009 |
Anthony D. JosephUniversity of California BerkeleyVerified email at eecs.berkeley.edu
