2013

• Yuanhua Lv, Dimitrios Lymberopoulos, Qiang Wu, and Jie Liu, Cluster-based Smoothing of Sparse Ranking Signals in Mobile Local Search, no. MSR-TR-2013-52, May 2013

2012

• Sahand Negahban, Benjamin Rubinstein, and Jim Gemmell, Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), ACM, 30 October 2012
• Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft, Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning, in Journal of Privacy and Confidentiality, vol. 4, no. 1, pp. 65-100, August 2012
• Benjamin Rubinstein and Aleksandr Simma, On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers, in IEEE Transactions on Information Theory, vol. 58, no. 7, pp. 4160-4163, IEEE, July 2012
• Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, and Peter L. Bartlett, A Learning-Based Approach to Reactive Security, in IEEE Transactions on Dependable and Secure Computing, vol. 9, no. 4, pp. 482-493, IEEE Computer Society, July 2012
• Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, and J. D. Tygar, Query Strategies for Evading Convex-Inducing Classifiers, in Journal of Machine Learning Research, vol. 13, no. May, pp. 1293−1332, MIT Press, May 2012
• Benjamin I. P. Rubinstein and J. Hyam Rubinstein, A Geometric Approach to Sample Compression, in Journal of Machine Learning Research, vol. 13, no. April, pp. 1221-1261, MIT Press, April 2012
• Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, and Jiawei Han, A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration, in Proc. 2012 International Conference on Very Large Data Bases (VLDB'12/PVLDB), vol. 5, no. February, pp. 550-561, Very Large Data Bases Endowment Inc., February 2012

2011

• Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar, Adversarial Machine Learning, in Proceedings of the 4th ACM Workshop on Artificial Intelligence and Security, ACM, 21 October 2011
• Adam Barth, Saung Li, Benjamin I. P. Rubinstein, and Dawn Song, How Open Should Open Source Be?, 31 August 2011
• Jim Gemmell, Benjamin I. P. Rubinstein, and Ashok K. Chandra, Improving Entity Resolution with Global Constraints, no. MSR-TR-2011-100, 30 August 2011
• Arvind Narayanan, Elaine Shi, and Benjamin Rubinstein, Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge, in Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), IEEE, 22 February 2011