Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Global Geometric Framework for Nonlinear Dimensionality Reduction
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).
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 of co-authors of John Langford
This figure shows the co-authorship network connecting the top 25 collaborators of John Langford.
A scholar is included among the top collaborators of John Langford based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with John Langford. John Langford is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Misra, Dipendra, Qinghua Liu, Chi Jin, & John Langford. (2021). Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. International Conference on Learning Representations.2 indexed citations
2.
Ash, Jordan T., Chicheng Zhang, Akshay Krishnamurthy, John Langford, & Alekh Agarwal. (2020). Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. International Conference on Learning Representations.22 indexed citations
3.
Chan, Justin, Dean P. Foster, Eric Horvitz, et al.. (2020). PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing. IEEE Data(base) Engineering Bulletin. 43(2). 15–35.2 indexed citations
4.
Misra, Dipendra, Mikael Henaff, Akshay Krishnamurthy, & John Langford. (2020). Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. International Conference on Machine Learning. 1. 6961–6971.6 indexed citations
5.
Sun, Wen, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, & John Langford. (2019). Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. Conference on Learning Theory. 2898–2933.9 indexed citations
6.
Krishnamurthy, Akshay, John Langford, Aleksandrs Slivkins, & Chicheng Zhang. (2019). Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. Journal of Machine Learning Research. 21(137). 1–2027.1 indexed citations
7.
Luo, Haipeng, Chen-Yu Wei, Alekh Agarwal, & John Langford. (2018). Efficient Contextual Bandits in Non-stationary Worlds. Conference on Learning Theory. 1739–1776.6 indexed citations
8.
Daumé, Hal, Nikos Karampatziakis, John Langford, & Paul Mineiro. (2017). Logarithmic Time One-Against-Some. International Conference on Machine Learning. 923–932.5 indexed citations
Zinkevich, Martin, John Langford, & Alex Smola. (2009). Slow Learners are Fast. Neural Information Processing Systems. 22. 2331–2339.97 indexed citations
14.
Langford, John, Tong Zhang, Daniel Hsu, & Sham M. Kakade. (2009). Multi-Label Prediction via Compressed Sensing. Neural Information Processing Systems. 22. 772–780.185 indexed citations
15.
Langford, John & Tong Zhang. (2007). The Epoch-Greedy algorithm for contextual multi-armed bandits. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 817–824.166 indexed citations
16.
Langford, John, et al.. (2006). Predicting conditional quantiles via reduction to classification. Uncertainty in Artificial Intelligence. 257–264.7 indexed citations
Langford, John, Martin Zinkevich, & Sham M. Kakade. (2002). Competitive Analysis of the Explore/Exploit Tradeoff. ScholarlyCommons (University of Pennsylvania). 339–346.
19.
Langford, John, Matthias Seeger, & Nimrod Megiddo. (2001). An Improved Predictive Accuracy Bound for Averaging Classifiers. International Conference on Machine Learning. 290–297.14 indexed citations
20.
Thrun, Sebastian, John Langford, & Dieter Fox. (1999). Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. International Conference on Machine Learning. 415–424.27 indexed citations
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.