Supervised Learning

• Theory

  John Langford. Practical Prediction Theory for Classification (A tutorial) lyx, tex, ps.gz, pdf slides
  • Avrim Blum and John Langford PAC-MDL Bounds COLT 2003 .ps.gz, .pdf, .tex
  • John Langford, Quantitatively Tight Sample Complexity Bounds Thesis .ps.gz, .pdf, .lyx, .tex, mathml (viewable with mozilla)
    • John Langford and Avrim Blum Microchoice Bounds and Self Bounding learning algorithms. COLT99, pages 209-214 and Machine Learning Journal (2002) .ps.gz, .pdf, .tex
    • John Langford, Matthias Seeger, and Nimrod Megiddo. An Improved Predictive Accuracy Bound for Averaging Classifiers ICML2001 .pdf, .ps.gz
    • John Langford and Matthias Seeger, Bounds for Averaging Classifiers Technical report, Carnegie Mellon 2001 .pdf, .ps.gz
    • John Langford and David McAllester. Computable Shell Decomposition Bounds. COLT2000 .pdf, .ps.gz, .tex
    • John Langford. Introductory Learning Theory. Neurocolt Workshop on bounds less than 0.5
    • John Langford Combining Train Set and Test Set Bounds. ICML2002 .ps.gz, .pdf, .tex
  • John Langford and John Shawe-Taylor PAC-Bayes and Margins. NIPS2002 .pdf, .ps.gz, .tex
  • Avrim Blum, Adam Kalai, and John Langford Beating the Holdout: Bounds for KFold and Progressive Cross-Validation. COLT99 pages 203-208, .pdf, .ps.gz, .tex
  • 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. .pdf, .ps.gz, .tex

• Algorithms

  • John Langford and Rich Caruana, (Not) Bounding the True Error NIPS2001 .pdf, .ps.gz, .tex
  • Joseph O'Sullivan, John Langford, Rich Caruana and Avrim Blum. FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML2000 .pdf, .ps.gz, .tex

Unsupervised Learning

• Manifold extraction

  • Josh Tenenbaum, Vin de Silva and John Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction . Science 290, 2319-2323, 2000 isomap site
    • a comment and response
  • Mira Bernstein, Vin de Silva, John Langford and Josh Tenenbaum Graph approximations to geodesics on embedded manifolds Technical report 2000

• Particle Filters

  • Sebastian Thrun, John Langford, and Vandi Verma, Risk Sensitive Particle Filters, NIPS2001. .pdf, .ps.gz, .tex
  • S. Thrun and John Langford, 1998. Monte Carlo Hidden Markov Models. Technical Report
    • S. Thrun, John Langford, and Dieter Fox 1999. Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Proecesses. ICML99

Reinforcement Learning

• Sham Kakade, Michael Kearns, and John Langford Exploration in Metric State Spaces ICML2003 .ps.gz, .pdf, .tex
• Sham Kakade, John Langford Approximately Optimal Approximate Reinforcement Learning ICML2002 .ps.gz, .pdf, .tex
• John Langford, Martin Zinkevich, Sham Kakade Competitive Analysis of the Explore/Exploit Tradeoff ICML2002 .ps.gz, .pdf, .tex

Planning

• Avrim Blum and John Langford Probabilistic Planning in the Graphplan Framework. ECP, 1999.

Steganography

• Nick Hopper, John Langford, and Luis von Ahn Provably Secure Steganography Crypto 2002/CMU Tech report .ps.gz, .pdf, .tex (revised corrections .tex w.r.t. crypto submission)

Game Theory

• Sham Kakade, Michael Kearns, John Langford, and Luis Ortiz, Correlated Equilibria in Graphical Games, ACM EC 2003. .ps.gz, .pdf, .tex

Quantum Computing

• John Langford Generic Quantum Block Compression (at xxx.lanl.gov and Phys. rev. A.)

Humans and Computers

• Luis von Ahn, Manuel Blum, Nick Hopper and John Langford CAPTCHA: Using Hard AI Problems for Security Eurocrypt 2003 .ps.gz, .pdf, .tex
• Luis von Ahn, Manuel Blum, and John Langford Telling Humans and Computers Apart (Automatically) CMU Tech Report .ps.gz, .pdf, .tex