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.
Pegasos: primal estimated sub-gradient solver for SVM
2010931 citationsShai Shalev‐Shwartz, Nathan Srebro et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Andrew Cotter'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 Andrew Cotter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Cotter more than expected).
This network shows the impact of papers produced by Andrew Cotter. 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 Andrew Cotter. The network helps show where Andrew Cotter may publish in the future.
Co-authorship network of co-authors of Andrew Cotter
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Cotter.
A scholar is included among the top collaborators of Andrew Cotter 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 Andrew Cotter. Andrew Cotter 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.
Narasimhan, Harikrishna, et al.. (2020). Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. Neural Information Processing Systems. 33. 8693–8703.2 indexed citations
2.
Narasimhan, Harikrishna, et al.. (2020). Pairwise Fairness for Ranking and Regression. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5248–5255.47 indexed citations
3.
Cotter, Andrew, Maya R. Gupta, & Harikrishna Narasimhan. (2019). On Making Stochastic Classifiers Deterministic. Neural Information Processing Systems. 32. 10910–10920.1 indexed citations
4.
Narasimhan, Harikrishna, Andrew Cotter, & Maya R. Gupta. (2019). Optimizing Generalized Rate Metrics with Three Players. Neural Information Processing Systems. 32. 10746–10757.4 indexed citations
5.
Cotter, Andrew, et al.. (2019). Shape Constraints for Set Functions. International Conference on Machine Learning. 1388–1396.5 indexed citations
6.
Cotter, Andrew, et al.. (2018). Constrained Interacting Submodular Groupings. International Conference on Machine Learning. 1068–1077.2 indexed citations
7.
Gupta, Maya R., Dara Bahri, Andrew Cotter, & Kevin Robert Canini. (2018). Diminishing Returns Shape Constraints for Interpretability and Regularization. Neural Information Processing Systems. 31. 6834–6844.10 indexed citations
8.
Canini, Kevin Robert, et al.. (2016). Fast and Flexible Monotonic Functions with Ensembles of Lattices. Neural Information Processing Systems. 29. 2919–2927.10 indexed citations
Goh, Gabriel, Andrew Cotter, Maya R. Gupta, & Michael P. Friedlander. (2016). Satisfying real-world goals with dataset constraints. Neural Information Processing Systems. 29. 2423–2431.9 indexed citations
11.
Cotter, Andrew, Maya R. Gupta, & Jan Pfeifer. (2016). A Light Touch for Heavily Constrained SGD. Conference on Learning Theory. 729–771.6 indexed citations
12.
Cotter, Andrew, Shai Shalev‐Shwartz, & Nati Srebro. (2013). Learning Optimally Sparse Support Vector Machines. International Conference on Machine Learning. 266–274.24 indexed citations
13.
Acar, Umut A., et al.. (2012). Dynamic well-spaced point sets. Computational Geometry. 46(6). 756–773.1 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.