• Jake Abernethy
• David Rosenberg
• Ben Rubinstein
• Ambuj Tewari
• Mikhail Traskin

• AdaBoost is Universally Consistent [Slides: ps, pdf]
  Presented at the 2006 Summer Institute held by the Institute of Information Science (IIS), Academia Sinica, Taipei, Taiwan., August 3, 2006.
• Pattern classification and large margin classifiers [Slides: ps]
  To be presented at the 2006 Machine Learning Summer School, National Taiwan University of Science and Technology, Taipei, Taiwan., August 2-3, 2006.
• Empirical Minimization and Risk Bounds [Slides: ps]
• Statistical Properties of Large Margin Classifiers [Slides: ps, pdf]
• Large Margin Classifiers: Convexity and Classification [Slides: ps, pdf]
• Large Margin Methods for Structured Classification: Exponentiated Gradient Algorithms [Slides: ps, ps.gz]
• Local Rademacher Averages and Empirical Minimization [Slides: ps, pdf]
• The Role of Convexity in Prediction Problems. [Slides: ps, ps.gz; Handouts: ps, ps.gz]
  Presented at UC Berkeley EECS Joint Colloquium Distinguished Lecture Series, September 17, 2003.
• Prediction Algorithms: Complexity, Concentration, and Convexity. [Slides: ps, ps.gz; Handouts: ps, ps.gz]
  Presented at SYSID2003: 13th IFAC Symposium on System Identification, Rotterdam, The Netherlands, 27-29 August, 2003.
  See: Extended abstract.
• Convexity, Classification, and Risk Bounds. [Slides: ps, ps.gz; Handouts: ps, ps.gz]
  Presented at Workshop on Advances in Machine Learning, Montreal, Canada, June 8-11, 2003, and AMS/IMS/SIAM Joint Summer Research Conference on Machine Learning, Statistics, and Discovery, Snowbird, Utah, June 22-26, 2003.
  See: Convexity, classification, and risk bounds. Peter L. Bartlett, Michael I. Jordan and Jon D. McAuliffe. Technical Report 638, Department of Statistics, U.C. Berkeley, 2003.
• NIPS'98 Tutorial (an introduction to learning theory)

• COLT 2005, Bertinoro, June 27-30, 2005.
  
• NIPS 2004, Vancouver, Dec 13-18, 2004.
  
• Mathematical Foundations of Learning Theory, Barcelona, June 18-23, 2004.
• IMS/Bernoulli Society Meeting (including a session on `Machine learning in complex structures'), Barcelona, July 26-31, 2004.
  

• Bernoulli
• Foundations and Trends in Machine Learning
• IEEE Transactions on Information Theory
• Journal of Machine Learning Research
• Mathematics of Control, Signals and Systems
• Journal of Artificial Intelligence Research

"Neural Network Learning: Theoretical Foundations", Martin Anthony and Peter Bartlett, Cambridge University Press, 1999.