Benjamin Rubinstein
RESEARCHER
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Microsoft Research, Silicon Valley
1288 Pear Avenue
Mountain View, CA 94043, USA
Work: [3 letter firstname].[lastname]@microsoft.com
Personal: [firstname].i.p.[lastname]@gmail.com
Ben earned the PhD in Computer Science from UC Berkeley under Peter Bartlett in the Spring of 2010. His dissertation research focused on machine learning in computer security, and was based on work with Adam Barth, Peter Bartlett, Anthony Joseph, Dawn Song and Doug Tygar at Berkeley, John Mitchell at Stanford, and Ling Huang and Nina Taft at Intel Labs Berkeley, among others. His current interests extend to applications of machine learning in privacy & security, databases, search, and basic research questions in learning & statistics.
Recent Activities
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Invitee, 2012 Perspectives Workshop - Machine Learning Methods for Computer Security, Schloss Dagstuhl, Germany
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Co-organizer, AISEC'2011 Workshop (co-located with CCS)
- Member, SIGMETRICS'2010 Shadow Program Committee
- Member, PSDML'2010 Program Committee (workshop co-located with ECML/PKDD)
Interns
- Sahand Negahban - UC Berkeley (2011) Now postdoc at MIT
- Duo Zhang - UIUC (2011)
- Bo Zhao - UIUC (2011)
Publications
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), 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
- Alvaro A. Cárdenas, Rachel Greenstadt, and Benjamin I. P. Rubinstein, Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, in Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, ACM, 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
- 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, 2011
- 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, MIT Press, 2011
- Benjamin I. P. Rubinstein and J. Hyam Rubinstein, A Geometric Approach to Sample Compression, in Journal of Machine Learning Research, MIT Press, 2011
- 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, IEEE Computer Society, 2011
2010
- Benjamin Rubinstein, Secure Learning and Learning for Security: Research in the Intersection, Dept. EECS, UC Berkeley, 13 May 2010
- Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven Lee, Satish Rao, Anthony Tran, and J. D. Tygar, Near Optimal Evasion of Convex-Inducing Classifiers, in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 2010
- 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 Proceedings of the Fourteenth International Conference on Financial Cryptography and Data Security (FC 2010), 2010
- Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, and J. D. Tygar, Classifier Evasion: Models and Open Problems, in ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, 2010
2009
- Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia, Misleading Learners: Co-opting Your Spam Filter, in Machine Learning in Cyber Trust: Security, Privacy, and Reliability, pp. 17–51, Springer, 2009
- Benjamin I. P. Rubinstein, Shifting in the n-Cube: Online Mistake Bounds and the Sample Compression Conjecture, 2009
- Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J. D. Tygar, Stealthy Poisoning Attacks on PCA-based Anomaly Detectors, in ACM SIGMETRICS Performance Evaluation Review, vol. 37, no. 2, pp. 73–74, 2009
- Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting: One-Inclusion Mistake Bounds and Sample Compression, in Journal of Computer and System Sciences, vol. 75, no. 1, pp. 37–59, 2009
- Arpita Ghosh, Benjamin I. P. Rubinstein, Sergei Vassilvitskii, and Martin Zinkevich, Adaptive Bidding for Display Advertising, in Proceedings of the 18th International World Wide Web Conference (WWW 2009), 2009
- Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J. D. Tygar, ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors, in Proceedings of the Ninth Internet Measurement Conference (IMC 2009), 2009
2008
- Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, and Doug Tygar, Compromising PCA-based Anomaly Detectors for Network-Wide Traffic, no. UCB/EECS-2008-73, May 2008
- Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, and J. D. Tygar, Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract), in Proceedings of the 11th International Symposium on Recent Advances in Intrusion Detection (RAID 2008), 2008
- Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia, Exploiting Machine Learning to Subvert Your Spam Filter, in First USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET'08), 2008
- Benjamin I. P. Rubinstein and J. Hyam Rubinstein, Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression, in Proceedings of the 21st Annual Conference on Learning Theory (COLT'08), 2008
- Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, and J. D. Tygar, Open Problems in the Security of Learning, in Proceedings of the 1st ACM Workshop on AISec (AISec 2008), 2008
2007
- Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting: One-Inclusion Mistake Bounds and Sample Compression, no. No. UCB/EECS-2007-86, June 2007
- Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds, in Advances in Neural Information Processing Systems 19 (NIPS 2006), 2007
2003
- Benjamin I. P. Rubinstein, Jon McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, and Terence P. Speed, Machine Learning in Low-level Microarray Analysis, in ACM SIGKDD Explorations (Special Issue on Microarray Data Mining), vol. 5, no. 2, pp. 130–139, December 2003
2001
- Benjamin I. P. Rubinstein, Evolving Quantum Circuits using Genetic Programming, in Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC2001), IEEE Press, 2001
2000
- Benjamin I. P. Rubinstein, Evolving Quantum Circuits using Genetic Programming, in Genetic Algorithms and Genetic Programming at Stanford 2000, pp. 325–334, Stanford Bookstore, Stanford University, CA, 2000
