BEN RUBINSTEIN

PUBLICATIONS

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Jump to:	[Machine Learning & Computer Security] [Computational Advertising, Internet Monetization] [Statistical/Computational Learning Theory] [Bioinformatics] [Computer Science Education] [Quantum Computing]

MACHINE LEARNING & COMPUTER SECURITY

[8]

Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song and Peter L. Bartlett. A Learning-Based Approach to Reactive Security, accepted in to the Fourteenth International Conference on Financial Cryptography and Data Security (FC 2010), 2009.

[7]

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 the Ninth Internet Measurement Conference (IMC 2009), pp. 1-14, 2009.

[6]

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, to appear in ACM SIGMETRICS Performance Evaluation Review, 2009.

[5]		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 Jeffrey J. P. Tsai and Philip S. Yu (eds.) Machine Learning in Cyber Trust: Security, Privacy, and Reliability, Springer, pg. 17-51, 2009.
[4]		Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein and J. D. Tygar. Open Problems in the Security of Learning, in the Proceedings of the 1st ACM Workshop on AISec, pp. 19-26, 2008.

[3]

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 the 11th International Symposium on Recent Advances in Intrusion Detection (RAID 2008), pp. 394-395, 2008. Winner of the RAID08 Best Poster Award.

[2]

Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft and J. D. Tygar. Compromising PCA-based Anomaly Detectors for Network-Wide Traffic, EECS Department, University of California, Berkeley, Technical Report No. UCB/EECS-2008-73, May 29, 2008.

[1]

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 the First USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET'08), 2008.

COMPUTATIONAL ADVERTISING, INTERNET MONETIZATION

[1]

Arpita Ghosh, Benjamin I. P. Rubinstein, Sergei Vassilvitskii and Martin Zinkevich. Adaptive Bidding for Display Advertising, in the Proceedings of the 18th International World Wide Web Conference (WWW 2009), pp. 251-260, 2009.

STATISTICAL/COMPUTATIONAL LEARNING THEORY

[6]

Benjamin I. P. Rubinstein and J. Hyam Rubinstein. A Geometric Approach to Sample Compression, submitted to the Journal of Machine Learning Research, November 2009.

[5]

Benjamin I. P. Rubinstein, Peter L. Bartlett and J. Hyam Rubinstein. Shifting: One-Inclusion Mistake Bounds and Sample Compression, invited paper in the Special Issue on Learning Theory 2006 in the Journal of Computer and System Sciences, 75(1), pp. 37-59, 2009.

[4]

Benjamin I. P. Rubinstein. Shifting in the n-Cube: Online Mistake Bounds and the Sample Compression Conjecture, University of Melbourne, MCS (by Research) Thesis, submitted Jan 2009, passed Oct 2009.

[3]

Benjamin I. P. Rubinstein and J. Hyam Rubinstein. Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression, in the 21st Annual Conference on Learning Theory (COLT'08), pp. 299-310, 2008.

[2]

Benjamin I. P. Rubinstein, Peter L. Bartlett and J. Hyam Rubinstein. Shifting: One-Inclusion Mistake Bounds and Sample Compression, EECS Department, University of California, Berkeley, Technical Report No. UCB/EECS-2007-86, June 25, 2007.

[1]

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.

BIOINFORMATICS

[1]

Benjamin I. P. Rubinstein, Jon McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao and Terence P. Speed. Machine Learning in Low-level Microarray Analysis, position paper in ACM SIGKDD Explorations (Special Issue on Microarray Data Mining), 5(2), December, 2003.

COMPUTER SCIENCE EDUCATION

[2]

Gad Abraham and Benjamin I. P. Rubinstein (eds). Proceedings of the Second Australian Undergraduate Students' Computing Conference, 175 pages, AUSCC, Australia, ISBN 0-9757173-0-8, December 2004.

[1]

Benjamin I. P. Rubinstein, Nelson Chan, and Kalyanaraman K. Kshetrapalapuram (eds). Proceedings of the First Australian Undergraduate Students' Computing Conference, 126 pages, AUSCC, Australia, ISBN 0-646-42751-2, September 2003.

QUANTUM COMPUTING

[2]

Benjamin I. P. Rubinstein. Evolving Quantum Circuits using Genetic Programming, in Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC2001), pp. 114-121, IEEE Press, 2001. Winner of the IEEE Computer Society's Lance Stafford Larson Scholarship 2002 for Best Undergraduate Computer Science Paper World-wide.

[1]

Benjamin I. P. Rubinstein. Evolving Quantum Circuits using Genetic Programming, in Genetic Algorithms and Genetic Programming at Stanford 2000, edited by John R. Koza, pp. 325-334, Stanford Bookstore, Stanford University, CA, 2000.

Maintained by:	Ben Rubinstein
Last updated:	Fri Nov 20 11:30:00 PDT 2009