Benjamin Rubinstein

Benjamin Rubinstein

Researcher, Microsoft Research

PhD, UC Berkeley

Interaction and Intent Group
Microsoft Research Silicon Valley
1288 Pear Ave., Mountain View, CA 94043
[firstname].i.p.[lastname]@gmail.com
MSR homepage LinkedIn

Juliet & Ella Lachlan

Ben earned the PhD in Computer Science from UC Berkeley under Peter Bartlett in 2010, collaborating closely with the SecML group, at the boundary of machine learning and security. Prior to that he completed undergraduate studies in his hometown at the University of Melbourne, Australia. Ben became a full-time Researcher at Microsoft Research, Silicon Valley 2010, where he has shipped production systems for entity resolution in Bing (search for "movies" here; TV, celebrities, events, wines are just a few other verticals) and the Xbox360 (driving [search for "entity matching"] huge success) and actively researches topics in machine learning, security, privacy, and databases.

Recent Service

Co-organizer, AISEC'2012, AISEC'2011 the ACM AI & Security Workshops (co-located with CCS)
Member, ICML'2012, ICML'2011 Program Committees
Invitee, 2012 Perspectives Workshop - Machine Learning Methods for Computer Security, Schloss Dagstuhl, Germany
Member, SIGMETRICS'2010 Shadow Program Committee
Member, PSDML'2010 Program Committee (workshop co-located with ECML/PKDD)
Member, AISEC'2010, AISEC'2009 Program Committees (workshops co-located with CCS)

Mentored Interns

Sahand Negahban - UC Berkeley (2011) Now postdoc at MIT
Duo Zhang - UIUC (2011)
Bo Zhao - UIUC (2011)

Publications

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, to appear 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, to appear 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, 9(4), pp. 482-493, July-Aug 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, MIT Press, to appear 2012

Benjamin I. P. Rubinstein and J. Hyam Rubinstein, A Geometric Approach to Sample Compression, in Journal of Machine Learning Research, 13, 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), 5(February), pp. 550-561, VLDB 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, pp. 43-54, 21 October 2011

Alvaro A. Cárdenas, Rachel Greenstadt, and Benjamin I. P. Rubinstein (eds.), Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 116 pages, ACM Press, October 2011

Adam Barth, Saung Li, Benjamin I. P. Rubinstein, and Dawn Song, How Open Should Open Source Be?, Technical Report UCB/EECS-2011-98, Dept. EECS, UC Berkeley, 31 August 2011

Jim Gemmell, Benjamin I. P. Rubinstein, and Ashok K. Chandra, Improving Entity Resolution with Global Constraints, Technical Report MSR-TR-2011-100, Microsoft Research, 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), pp. 1825-1834, IEEE, 22 February 2011

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
Benjamin Rubinstein, Secure Learning and Learning for Security: Research in the Intersection, PhD Dissertation, Dept. EECS, UC Berkeley, 13 May 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, 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), pp. 549-556, 2010

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), pp. 1-14, 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), pp. 251-260, 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, 75(1), pp. 37–59, 2009 [2009 erratum] Previously:

Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting: One-Inclusion Mistake Bounds and Sample Compression, Technical Report UCB/EECS-2007-86, Dept. EECS, UC Berkeley, June 2007

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, book chapter in Machine Learning in Cyber Trust: Security, Privacy, and Reliability, pp. 17–51, Springer, 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, 37(2), pp. 73–74, 2009
Benjamin I. P. Rubinstein, Shifting in the n-Cube: Online Mistake Bounds and the Sample Compression Conjecture, Masters Research thesis, Faculty of Engineering, University of Melbourne, 2009

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), pp. 394-395, 2008 winner of RAID08 best poster award
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), pp. 19-26, 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, Technical Report UCB/EECS-2008-73, Dept. EECS, UC Berkeley, May 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), pp. 299-310, 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

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), pp. 1193-1200, 2007

Pre 2007

Gad Abraham and Benjamin I. P. Rubinstein (eds.), Proceedings of the Second Australian Students' Computing Conference (AUSCC), ISBN 0-975-71730-8, 175 pages, 2004
Benjamin I. P. Rubinstein, Nelson Chan, and K. K. Kshetrapalapuram (eds.), Proceedings of the First Australian Undergraduate Students' Computer Conference (AUSCC), ISBN 0-646-42751-2, 126 pages, 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), 5(2), pp. 130–139, December 2003
Benjamin I. P. Rubinstein, Evolving Quantum Circuits using Genetic Programming, in Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC2001), IEEE Press, 2001 winner of IEEE Computer Society's Lance Stafford Larson Scholarship for best ugrad student paper worldwide. Previously:

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

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