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.
Introduction to Derivative-Free Optimization
20091.1k citationsAndrew R. Conn, Katya Scheinberg 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 Katya Scheinberg
Since
Specialization
Citations
This map shows the geographic impact of Katya Scheinberg'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 Katya Scheinberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katya Scheinberg more than expected).
Fields of papers citing papers by Katya Scheinberg
This network shows the impact of papers produced by Katya Scheinberg. 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 Katya Scheinberg. The network helps show where Katya Scheinberg may publish in the future.
Co-authorship network of co-authors of Katya Scheinberg
This figure shows the co-authorship network connecting the top 25 collaborators of Katya Scheinberg.
A scholar is included among the top collaborators of Katya Scheinberg 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 Katya Scheinberg. Katya Scheinberg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nguyen, Lam M., Phuong Ha Nguyen, Marten van Dijk, et al.. (2018). SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 3750–3758.14 indexed citations
Scheinberg, Katya. (2006). An Efficient Implementation of an Active Set Method for SVMs. Journal of Machine Learning Research. 7(80). 2237–2257.43 indexed citations
Fine, Shai & Katya Scheinberg. (2002). Efficient svm training using low-rank kernel representations. Journal of Machine Learning Research. 2(2). 243–264.388 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.