- John Langford and John Shawe-Taylor PAC-Bayes and Margins. Neural Information Processing Systems (NIPS2002)
- Sham Kakade, John Langford Approximately Optimal Approximate Reinforcement Learning International Conference on Machine Learning (ICML2002)
- John Langford, Martin Zinkevich, Sham Kakade Competitive Analysis of the Explore/Exploit Tradeoff International Conference on Machine Learning (ICML2002)
- Nick Hopper, John Langford, and Luis von Ahn Provably Secure Steganography Crypto 2002
- Sebastian Thrun, John Langford, and Vandi Verma, Risk Sensitive Particle Filters, Neural Information Processing Systems (NIPS2001).
- John Langford and Rich Caruana, (Not) Bounding the True Error Neural Information Processing Systems (NIPS2001)
- John Langford, Matthias Seeger, and Nimrod Megiddo. An Improved Predictive Accuracy Bound for Averaging Classifiers International Conference on Machine Learning (ICML2001)
- Josh Tenenbaum, Vin de Silva and John Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction . Science 290, pages 2319-2323, 2000 isomap site
- John Langford and David McAllester. Computable Shell Decomposition Bounds. Computational Learning Theory (COLT2000)
- Joseph O'Sullivan, John Langford, Rich Caruana and Avrim Blum. FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. International Conference on Machine Learning (ICML2000)
- John Langford and Avrim Blum 1999. Microchoice Bounds and Self Bounding learning algorithms. COLT99 journal draft accepted at Machine Learning Journal
- Avrim Blum, Adam Kalai, and John Langford 1999. Beating the Holdout: Bounds for KFold and Progressive Cross-Validation. Computational Learning Theory (COLT99)
- S. Thrun, John Langford, and Dieter Fox 1999. Monte
Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Proecesses. International Conference on Machine Learning (ICML99)
- Avrim Blum, Carl Burch, and John Langford, 1998. On Learning Monotone Boolean Functions
Proceedings of the 39th Annual Symposium on Foundations of
Computer Science FOCS '98.
- Avrim Blum and John Langford Probabilistic Planning in the
Graphplan Framework. European Conference on Planning (ECP), 1999.
IBM, TJ Watson, Yorktown, NY September 2002 - Present
Research on many topics
University of Pennsylvania, Philadelphia, PA June 2002 - August 2002
Research with Michael Kearns on game theory and reinforcement learning
Carnegie Mellon, Pittsburgh, PA September 1997 - May 2002
PhD in machine learning theory.
ATnT Shannon Labs, Florham Park, NJ Summer 2001
Learning theory and sample complexity bounds
IBM, Almaden, CA Summer 2000
Summer research with Shiv Vaithyanathan and Nimrod Megiddo
Hidden markov models for parsing and PAC Averaging bounds.
California Institute of Technology, Pasadena, CA, summer 1997
Research with Shuki Bruck and Yaser Abu-Mustafa
Researched the Support Vector machine algorithm and the application of learning bounds to it.
California Institute of Technology, Pasadena, CA, summer 1996
Research supported by SURF
Developed a Monte Carlo generator for theorized Heavy Majorana Neutrinos in an e+e- collider.
California Institute of Technology, Pasadena, CA, summer 1995
Researcher for Mani Chandy
Developed a Games Archetype which can be used with a board evaluation
function to create 2 player perfect information games quickly.
California Institute of Technology, Pasadena, CA, summer 1994
Researcher supported by SURF
Implemented a new version of the Parti-game algorithm by Andrew Moore and explored its use.