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Computer Science

D-Index
78
Citations
59356
World Ranking
1161
National Ranking
615

Overview

John Langford is a researcher affiliated with Microsoft in the United States. Their work primarily spans the field of Computer Science, with a focus on Artificial Intelligence and related subfields. Langford has contributed extensively to areas such as Management Science and Operations Research, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, and Cognitive Neuroscience.

Their research topics cover a diverse range of areas including:

  • Advanced Bandit Algorithms Research
  • Data Stream Mining Techniques
  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Machine Learning and Data Classification
  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data

Langford has authored numerous papers with a significant presence in prominent publication venues. These include:

  • arXiv (Cornell University) - 30 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence - 1 publication
  • bioRxiv (Cold Spring Harbor Laboratory) - 1 publication
  • Journal of Vascular and Interventional Radiology - 1 publication

Among their recent research papers are:

  • "PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing," 2020, arXiv (Cornell University)
  • "Federated Residual Learning," 2020, arXiv (Cornell University)
  • "Position Paper: Agent AI Towards a Holistic Intelligence," 2024, arXiv (Cornell University)
  • "Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning," 2022, arXiv (Cornell University)
  • "Efficient Contextual Bandits with Continuous Actions," 2020, arXiv (Cornell University)

Frequent collaborations form an important aspect of Langford's research. Key co-authors include:

  • Alex Lamb (9 joint publications)
  • Dipendra Misra (7 joint publications)
  • Akshay Krishnamurthy (6 joint publications)
  • Paul Mineiro (6 joint publications)
  • Rajan Chari (5 joint publications)

Best Publications

  • A global geometric framework for nonlinear dimensionality reduction.

    J. B. Tenenbaum;V. de Silva;J. C. Langford

  • Proceedings of the 29th International Conference on Machine Learning (ICML-12)

    John Langford;Joelle Pineau

  • A contextual-bandit approach to personalized news article recommendation

    Lihong Li;Wei Chu;John Langford;Robert E. Schapire

  • CAPTCHA: using hard AI problems for security

    Luis Von Ahn;Manuel Blum;Nicholas J. Hopper;John Langford

  • Telling humans and computers apart automatically

    Luis von Ahn;Manuel Blum;John Langford

  • Agnostic active learning

    Maria-Florina Balcan;Alina Beygelzimer;John Langford

  • Feature hashing for large scale multitask learning

    Kilian Weinberger;Anirban Dasgupta;John Langford;Alex Smola

  • Cover trees for nearest neighbor

    Alina Beygelzimer;Sham Kakade;John Langford

  • Cost-sensitive learning by cost-proportionate example weighting

    B. Zadrozny;J. Langford;N. Abe

  • Approximately Optimal Approximate Reinforcement Learning

    Sham Kakade;John Langford

  • The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information

    John Langford;Tong Zhang

  • Search-based structured prediction

    Hal Daumé;John Langford;Daniel Marcu

  • Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

    Lihong Li;Wei Chu;John Langford;Xuanhui Wang

  • Sparse Online Learning via Truncated Gradient

    John Langford;Lihong Li;Tong Zhang

  • PAC model-free reinforcement learning

    Alexander L. Strehl;Lihong Li;Eric Wiewiora;John Langford

  • Scaling up machine learning: parallel and distributed approaches

    Ron Bekkerman;Mikhail Bilenko;John Langford

  • Doubly Robust Policy Evaluation and Learning

    John Langford;Lihong Li;Miroslav Dud k

  • Multi-Label Prediction via Compressed Sensing

    John Langford;Tong Zhang;Daniel J. Hsu;Sham M Kakade

  • A Reductions Approach to Fair Classification

    Alekh Agarwal;Alina Beygelzimer;Miroslav Dudík;John Langford

  • Outlier detection by active learning

    Naoki Abe;Bianca Zadrozny;John Langford

  • Doubly Robust Policy Evaluation and Learning

    Miroslav Dudik;John Langford;Lihong Li

  • Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

    Jordan T. Ash;Chicheng Zhang;Akshay Krishnamurthy;John Langford

  • Telling Humans and Computers Apart Automatically or How Lazy Cryptographers do AI

    Louis von Ahn;Manuel Blum;John Langford

  • Feature Hashing for Large Scale Multitask Learning

    Kilian Weinberger;Anirban Dasgupta;Josh Attenberg;John Langford

Frequent Co-Authors

Alekh Agarwal
Alekh Agarwal Google (United States)
Akshay Krishnamurthy
Akshay Krishnamurthy Microsoft (United States)
Lihong Li
Lihong Li Amazon (United States)
Hal Daumé
Hal Daumé University of Maryland, College Park
Miroslav Dudík
Miroslav Dudík Microsoft (United States)
Robert E. Schapire
Robert E. Schapire Microsoft (United States)
Daniel Hsu
Daniel Hsu Columbia University
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Sham M. Kakade
Sham M. Kakade Harvard University
Nicholas Hopper
Nicholas Hopper University of Minnesota

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