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.
This map shows the geographic impact of John Lafferty'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 Lafferty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Lafferty more than expected).
This network shows the impact of papers produced by John Lafferty. 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 Lafferty. The network helps show where John Lafferty may publish in the future.
Co-authorship network of co-authors of John Lafferty
This figure shows the co-authorship network connecting the top 25 collaborators of John Lafferty.
A scholar is included among the top collaborators of John Lafferty 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 Lafferty. John Lafferty is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Fan, Zhou, et al.. (2019). Surfing: Iterative Optimization Over Incrementally Trained Deep Networks. Neural Information Processing Systems. 32. 15008–15017.3 indexed citations
4.
Chatterjee, Sabyasachi, et al.. (2018). Prediction Rule Reshaping. International Conference on Machine Learning. 1014–1022.
5.
Kalaitzis, Freddie, John Lafferty, Neil D. Lawrence, & Shuheng Zhou. (2013). The Bigraphical Lasso. International Conference on Machine Learning. 28. 1229–1237.3 indexed citations
6.
Gu, Haijie & John Lafferty. (2012). Sequential Nonparametric Regression. International Conference on Machine Learning. 387–394.2 indexed citations
7.
Liu, Han, Larry Wasserman, & John Lafferty. (2012). Exponential Concentration for Mutual Information Estimation with Application to Forests. Neural Information Processing Systems. 25. 2537–2545.11 indexed citations
8.
Verducci, Joseph S., et al.. (2007). Prediction and discovery : AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25-29, 2006, Snowbird, Utah. American Mathematical Society eBooks.4 indexed citations
9.
Wasserman, Larry & John Lafferty. (2007). Statistical Analysis of Semi-Supervised Regression. Neural Information Processing Systems. 20. 801–808.81 indexed citations
10.
Wainwright, Martin J., John Lafferty, & Pradeep Ravikumar. (2006). High-Dimensional Graphical Model Selection Using ell_1-Regularized Logistic Regression. Neural Information Processing Systems. 19. 1465–1472.105 indexed citations
11.
Wasserman, Larry & John Lafferty. (2005). Rodeo: Sparse Nonparametric Regression in High Dimensions. Neural Information Processing Systems. 18. 707–714.10 indexed citations
Zhu, Xiaojin, John Lafferty, & Zoubin Ghahramani. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. UCL Discovery (University College London).317 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.