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.
Generalized Additive Models.
19916.2k citationsRyan J. Tibshirani et al.profile →
An Introduction to the Bootstrap.
19942.6k citationsRyan J. Tibshirani et al.profile →
Strong Rules for Discarding Predictors in Lasso-Type Problems
2011393 citationsRobert Tibshirani, Jacob Bien et al.profile →
A significance test for the lasso
2014362 citationsRichard Lockhart, Jonathan Taylor et al.profile →
Distribution-Free Predictive Inference for Regression
2017353 citationsAlessandro Rinaldo, Ryan J. Tibshirani et al.profile →
Surprises in high-dimensional ridgeless least squares interpolation
2022134 citationsRyan J. Tibshirani et al.profile →
Conformal prediction beyond exchangeability
202376 citationsRina Foygel Barber, Emmanuel J. Candès et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ryan J. Tibshirani
Since
Specialization
Citations
This map shows the geographic impact of Ryan J. Tibshirani'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 Ryan J. Tibshirani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan J. Tibshirani more than expected).
Fields of papers citing papers by Ryan J. Tibshirani
This network shows the impact of papers produced by Ryan J. Tibshirani. 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 Ryan J. Tibshirani. The network helps show where Ryan J. Tibshirani may publish in the future.
Co-authorship network of co-authors of Ryan J. Tibshirani
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan J. Tibshirani.
A scholar is included among the top collaborators of Ryan J. Tibshirani 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 Ryan J. Tibshirani. Ryan J. Tibshirani is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wei, Yuting, et al.. (2021). Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression.. International Conference on Artificial Intelligence and Statistics. 3178–3186.7 indexed citations
7.
Ali, Alnur, Edgar Dobriban, & Ryan J. Tibshirani. (2020). The Implicit Regularization of Stochastic Gradient Flow for Least Squares. International Conference on Machine Learning. 1. 233–244.3 indexed citations
8.
Jahja, Maria, David Farrow, Roni Rosenfeld, & Ryan J. Tibshirani. (2019). Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. Neural Information Processing Systems. 32. 13166–13175.1 indexed citations
Wang, Yu-Xiang, et al.. (2017). Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods.. Neural Information Processing Systems. 30. 5800–5810.5 indexed citations
Tibshirani, Ryan J.. (2017). Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. Neural Information Processing Systems. 30. 517–528.4 indexed citations
13.
Padilla, Oscar Hernán Madrid, James G. Scott, James Sharpnack, & Ryan J. Tibshirani. (2016). The DFS fused lasso: nearly optimal linear-time denoising over graphs and trees. arXiv (Cornell University).1 indexed citations
14.
Wang, Yu-Xiang, et al.. (2016). Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. Neural Information Processing Systems. 29. 3513–3521.2 indexed citations
15.
Wang, Yuxiang, James Sharpnack, Alexander J. Smola, & Ryan J. Tibshirani. (2015). Trend Filtering on Graphs. Journal of Machine Learning Research. 17(1). 1042–1050.10 indexed citations
Taylor, Jonathan, Richard Lockhart, Ryan J. Tibshirani, & Robert Tibshirani. (2014). Post-selection adaptive inference for Least Angle Regression and the Lasso. arXiv (Cornell University).18 indexed citations
18.
Taylor, Jonathan, Richard Lockhart, Ryan J. Tibshirani, & Robert Tibshirani. (2014). Exact Post-selection Inference for Forward Stepwise and Least Angle Regression. arXiv (Cornell University).8 indexed citations
19.
Tibshirani, Ryan J. & Jonathan Taylor. (2010). Regularization Paths for Least Squares Problems with Generalized $\ell_1$ Penalties. arXiv (Cornell University).1 indexed citations
20.
Tibshirani, Ryan J.. (1985). How Many Bootstraps?. Defense Technical Information Center (DTIC).2 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.