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.
Maximum entropy modeling of species geographic distributions
200513.3k citationsRobert E. Schapire et al.profile →
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
199712.7k citationsYoav Freund, Robert E. SchapireJournal of Computer and System Sciencesprofile →
Countries citing papers authored by Robert E. Schapire
Since
Specialization
Citations
This map shows the geographic impact of Robert E. Schapire'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 Robert E. Schapire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert E. Schapire more than expected).
Fields of papers citing papers by Robert E. Schapire
This network shows the impact of papers produced by Robert E. Schapire. 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 Robert E. Schapire. The network helps show where Robert E. Schapire may publish in the future.
Co-authorship network of co-authors of Robert E. Schapire
This figure shows the co-authorship network connecting the top 25 collaborators of Robert E. Schapire.
A scholar is included among the top collaborators of Robert E. Schapire 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 Robert E. Schapire. Robert E. Schapire 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.
Hazan, Elad, et al.. (2021). Multiclass Boosting and the Cost of Weak Learning. Neural Information Processing Systems. 34.2 indexed citations
2.
Ji, Ziwei, Miroslav Dudı́k, Robert E. Schapire, & Matus Telgarsky. (2020). Gradient descent follows the regularization path for general losses.. Conference on Learning Theory. 2109–2136.
3.
Foster, Dylan J., Alekh Agarwal, Miroslav Dudı́k, Haipeng Luo, & Robert E. Schapire. (2018). Practical Contextual Bandits with Regression Oracles. International Conference on Machine Learning. 1534–1543.10 indexed citations
4.
Feige, Uriel, Yishay Mansour, & Robert E. Schapire. (2015). Learning and inference in the presence of corrupted inputs. Conference on Learning Theory. 40(2015). 637–657.5 indexed citations
5.
Rudin, Cynthia, et al.. (2013). The rate of convergence of AdaBoost. Journal of Machine Learning Research. 14(1). 2315–2347.21 indexed citations
6.
Schapire, Robert E.. (2010). The Convergence Rate of AdaBoost.. Conference on Learning Theory. 308–309.7 indexed citations
7.
Bradley, Joseph K. & Robert E. Schapire. (2007). FilterBoost: Regression and Classification on Large Datasets. Neural Information Processing Systems. 20. 185–192.50 indexed citations
8.
Lozano, Aurélie, Sanjeev R. Kulkarni, & Robert E. Schapire. (2005). Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. Neural Information Processing Systems. 18. 819–826.22 indexed citations
9.
McAllester, David & Robert E. Schapire. (2003). Learning theory and language modeling. Morgan Kaufmann Publishers Inc. eBooks. 271–287.3 indexed citations
10.
Rudin, Cynthia, Ingrid Daubechies, & Robert E. Schapire. (2003). On the Dynamics of Boosting. Neural Information Processing Systems. 16. 1101–1108.5 indexed citations
11.
Collins, Michael, Sanjoy Dasgupta, & Robert E. Schapire. (2001). A Generalization of Principal Components Analysis to the Exponential Family. Neural Information Processing Systems. 14. 617–624.230 indexed citations
12.
Schapire, Robert E. & Yoram Singer. (2000). BoosTexter: A Boosting-based System for Text Categorization. Machine Learning. 39(2-3). 135–168.1458 indexed citations breakdown →
McAllester, David & Robert E. Schapire. (2000). On the Convergence Rate of Good-Turing Estimators. Conference on Learning Theory. 1–6.71 indexed citations
15.
Abney, Steven, Robert E. Schapire, & Yoram Singer. (1999). Boosting Applied to Tagging and PP Attachment. Empirical Methods in Natural Language Processing.70 indexed citations
16.
Freund, Yoav & Robert E. Schapire. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences. 55(1). 119–139.12745 indexed citations breakdown →
17.
Schapire, Robert E.. (1997). Using output codes to boost multiclass learning problems. International Conference on Machine Learning. 313–321.157 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.