Benjamin I. P. Rubinstein

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han: A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. PVLDB 5(6): 550-561 (2012)

Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security. IEEE Trans. Dependable Sec. Comput. 9(4): 482-493 (2012)

Benjamin I. P. Rubinstein, Aleksandr Simma: On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers. IEEE Transactions on Information Theory 58(7): 4160-4163 (2012)

Alvaro A. Cárdenas, Blaine Nelson, Benjamin I. P. Rubinstein: Fifth ACM workshop on artificial intelligence and security (AISec 2012). ACM Conference on Computer and Communications Security 2012: 1056-1057

Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell: Scaling multiple-source entity resolution using statistically efficient transfer learning. CIKM 2012: 2224-2228

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han: A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. CoRR abs/1203.0058 (2012)

Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell: Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning. CoRR abs/1208.1860 (2012)

Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. Tygar: Adversarial machine learning. AISec 2011: 43-58

Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein: Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge. IJCNN 2011: 1825-1834

Yan Chen, Alvaro A. Cárdenas, Rachel Greenstadt, Benjamin I. P. Rubinstein (Eds.): Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, AISec 2011, Chicago, IL, USA, October 21, 2011. ACM 2011, isbn 978-1-4503-1003-1

Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein: Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge. CoRR abs/1102.4374 (2011)

Jim Gemmell, Benjamin I. P. Rubinstein, Ashok K. Chandra: Improving Entity Resolution with Global Constraints. CoRR abs/1108.6016 (2011)

Adam Barth, Saung Li, Benjamin I. P. Rubinstein, Dawn Song: How Open Should Open Source Be? CoRR abs/1109.0507 (2011)

Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]. J. Comput. Syst. Sci. 76(3-4): 278-280 (2010)

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. Doug Tygar: Near-Optimal Evasion of Convex-Inducing Classifiers. Journal of Machine Learning Research - Proceedings Track 9: 549-556 (2010)

Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security. Financial Cryptography 2010: 192-206

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, J. D. Tygar: Classifier Evasion: Models and Open Problems. PSDML 2010: 92-98

Benjamin I. P. Rubinstein, Aleksandr Simma: On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers. CoRR abs/1002.2044 (2010)

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. D. Tygar: Near-Optimal Evasion of Convex-Inducing Classifiers. CoRR abs/1003.2751 (2010)

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar: Query Strategies for Evading Convex-Inducing Classifiers. CoRR abs/1007.0484 (2010)

Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Shifting: One-inclusion mistake bounds and sample compression. J. Comput. Syst. Sci. 75(1): 37-59 (2009)

Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar: Stealthy poisoning attacks on PCA-based anomaly detectors. SIGMETRICS Performance Evaluation Review 37(2): 73-74 (2009)

Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar: ANTIDOTE: understanding and defending against poisoning of anomaly detectors. Internet Measurement Conference 2009: 1-14

Arpita Ghosh, Benjamin I. P. Rubinstein, Sergei Vassilvitskii, Martin Zinkevich: Adaptive bidding for display advertising. WWW 2009: 251-260

Benjamin I. P. Rubinstein, J. Hyam Rubinstein: A Geometric Approach to Sample Compression. CoRR abs/0911.3633 (2009)

Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft: Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. CoRR abs/0911.5708 (2009)

Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Xiaodong Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security. CoRR abs/0912.1155 (2009)

Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, J. Doug Tygar: Open problems in the security of learning. AISec 2008: 19-26

J. Hyam Rubinstein, Benjamin I. P. Rubinstein: Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression. COLT 2008: 299-310

Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles A. Sutton, J. Doug Tygar, Kai Xia: Exploiting Machine Learning to Subvert Your Spam Filter. LEET 2008

Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, J. Doug Tygar: Evading Anomaly Detection through Variance Injection Attacks on PCA. RAID 2008: 394-395

Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. NIPS 2006: 1193-1200

Benjamin I. P. Rubinstein, Jon D. McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed: Machine learning in low-level microarray analysis. SIGKDD Explorations 5(2): 130-139 (2003)