John Langford
- Artificial Intelligence top 0.05%
- Machine Learning and Algorithms 51
- Machine Learning and Data Classification 22
- Reinforcement Learning in Robotics 17
- Imbalanced Data Classification Techniques 13
- Algorithms and Data Compression 12
- Data Stream Mining Techniques 7
- Signal Processing top 0.2%
- Computational Mathematics top 1%
- Media Technology top 0.2%
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- Advanced Bandit Algorithms Research 29
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- Computability, Logic, AI Algorithms 6
- Co-authors
- Joshua B. TenenbaumVin de SilvaSham M. KakadeAlina BeygelzimerBianca ZadroznyNaoki AbeManuel BlumLuis von Ahn
- Journals
- Machine Learning (4 papers)Communications of the ACM (4 papers)Journal of Machine Learning Research (4 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
John Langford
109 papers receiving 14.1k citations
Hit Papers
Peers
Comparison fields: 5 of 209
- Computer Vision and Pattern Recognition 6.0k
- Artificial Intelligence 7.2k
- Signal Processing 1.8k
- Computational Mathematics 85
- Media Technology 977
Countries citing papers authored by John Langford
This map shows the geographic impact of John Langford's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Langford with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Langford more than expected).
Fields of papers citing papers by John Langford
This network shows the impact of papers produced by John Langford. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Langford. The network helps show where John Langford may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Langford, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Provable Rich Observation Reinforcement Learning with Combinatorial Latent States | 2021 | 2 |
| 2 | PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing | 2020 | 2 |
| 3 | Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches | 2019 | 9 |
| 4 | Efficient Contextual Bandits in Non-stationary Worlds | 2018 | 6 |
| 5 | Obtaining Reliable Estimates of Intact Tensile Strength | 2014 | 7 |
| 6 | 2012 | 6 | |
| 7 | Importance Weight Aware Gradient Updates | 2010 | 4 |
| 8 | The Epoch-Greedy algorithm for contextual multi-armed bandits | 2007 | 166 |
| 9 | The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information | 2007 | 201 |
| 10 | Predicting conditional quantiles via reduction to classification | 2006 | 7 |
| 11 | Tutorial on Practical Prediction Theory for Classification | 2005 | 147 |
| 12 | Estimating Class Membership Probabilities using Classifier Learners. | 2005 | 20 |
| 13 | PAC Bayes and Margins | 2003 | 35 |
| 14 | Exploration in metric state spaces | 2003 | 49 |
| 15 | Approximately Optimal Approximate Reinforcement Learning | 2002 | 217 |
| 16 | Competitive Analysis of the Explore/Exploit Tradeoff | 2002 | 0 |
| 17 | Risk Sensitive Particle Filters | 2001 | 50 |
| 18 | An Improved Predictive Accuracy Bound for Averaging Classifiers | 2001 | 14 |
| 19 | Computable Shell Decomposition Bounds | 2000 | 6 |
| 20 | Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes | 1999 | 27 |
About John Langford
John Langford is a scholar working on Management Science and Operations Research, Artificial Intelligence and Signal Processing, having authored 115 papers that have together received 15.1k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (51 papers), Advanced Bandit Algorithms Research (29 papers), Machine Learning and Data Classification (22 papers), Reinforcement Learning in Robotics (17 papers), Imbalanced Data Classification Techniques (13 papers), Algorithms and Data Compression (12 papers), Data Stream Mining Techniques (7 papers) and Computability, Logic, AI Algorithms (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.0k citations), Artificial Intelligence (7.2k citations) and Signal Processing (1.8k citations). John Langford has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Joshua B. Tenenbaum, Vin de Silva, Sham M. Kakade, Alina Beygelzimer, Bianca Zadrozny, Naoki Abe, Manuel Blum, Luis von Ahn, Tong Zhang and Alex Smola. Their work appears in journals such as Machine Learning, Communications of the ACM, Journal of Machine Learning Research, International Journal of Rock Mechanics and Mining Sciences and Algorithmica.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.