Langford, John

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science

Face and Expression RecognitionTopological and Geometric Data Analysis

Cited by 13,585

A contextual-bandit approach to personalized news article recommendation

Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010) · Proceedings of the 19th international conference on World wide web

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 2,562

Cost-sensitive learning by cost-proportionate example weighting

Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 680

Beating the hold-out: bounds for K-fold and progressive cross-validation

Blum, A., Kalai, A., & Langford, J. (1999) · Proceedings of the twelfth annual conference on Computational learning theory

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 265

Provably Secure Steganography

Hopper, N. J., Langford, J., & von Ahn, L. (2002) · Lecture Notes in Computer Science

Cryptography and Data SecurityAdvanced Steganography and Watermarking Techniques

Cited by 198

Contextual Decision Processes with low Bellman rank are PAC-Learnable

Jiang, N., Krishnamurthy, A., Agarwal, A., Langford, J., & Schapire, R. E. (2016) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 144

Provably Secure Steganography

Hopper, N., von Ahn, L., & Langford, J. (2009) · IEEE Transactions on Computers

Cryptography and Data SecurityAdvanced Steganography and Watermarking Techniques

Cited by 76

The arbitrariness of reviews, and advice for school administrators

Langford, J., & Guzdial, M. (2015) · Communications of the ACM

Research, Science, and AcademiaPublishing and Scholarly Communication

Cited by 49

PAC Reinforcement Learning with Rich Observations

Krishnamurthy, A., Agarwal, A., & Langford, J. (2016) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 13

Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models

Lamb, A., Islam, R., Efroni, Y., Didolkar, A., Misra, D., Foster, D., Molu, L., Chari, R., Krishnamurthy, A., & Langford, J. (2022) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 1