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
This map shows the geographic impact of Nathan Srebro'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 Nathan Srebro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Srebro more than expected).
This network shows the impact of papers produced by Nathan Srebro. 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 Nathan Srebro. The network helps show where Nathan Srebro may publish in the future.
Co-authorship network of co-authors of Nathan Srebro
This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Srebro.
A scholar is included among the top collaborators of Nathan Srebro 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 Nathan Srebro. Nathan Srebro is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hanneke, Steve, et al.. (2019). VC Classes are Adversarially Robustly Learnable, but Only Improperly. Conference on Learning Theory. 2512–2530.1 indexed citations
3.
Woodworth, Blake, Vitaly Feldman, Saharon Rosset, & Nathan Srebro. (2018). The Everlasting Database: Statistical Validity at a Fair Price. arXiv (Cornell University). 31. 6531–6540.1 indexed citations
4.
Blum, Avrim, Suriya Gunasekar, Thodoris Lykouris, & Nathan Srebro. (2018). On preserving non-discrimination when combining expert advice. Neural Information Processing Systems. 31. 8376–8387.2 indexed citations
5.
Neyshabur, Behnam, Srinadh Bhojanapalli, & Nathan Srebro. (2017). A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. International Conference on Learning Representations.40 indexed citations
6.
Wang, Jialei, Mladen Kolar, Nathan Srebro, & Tong Zhang. (2017). Efficient distributed learning with sparsity. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 7. 3636–3645.19 indexed citations
7.
Bhojanapalli, Srinadh, Behnam Neyshabur, & Nathan Srebro. (2016). Global optimality of local search for low rank matrix recovery. arXiv (Cornell University). 29. 3880–3888.57 indexed citations
8.
Neyshabur, Behnam & Nathan Srebro. (2015). On Symmetric and Asymmetric LSHs for Inner Product Search. International Conference on Machine Learning. 1926–1934.32 indexed citations
9.
Neyshabur, Behnam, Ryota Tomioka, & Nathan Srebro. (2015). Norm-Based Capacity Control in Neural Networks. Conference on Learning Theory. 1376–1401.51 indexed citations
10.
Neyshabur, Behnam & Nathan Srebro. (2014). A simpler and better LSH for Maximum Inner Product Search (MIPS). arXiv (Cornell University).2 indexed citations
Peng, Jian, Tamir Hazan, Nathan Srebro, & Jinbo Xu. (2012). Approximate inference by intersecting semidefinite bound and local polytope. International Conference on Artificial Intelligence and Statistics. 22. 868–876.4 indexed citations
13.
Jalali, Ali & Nathan Srebro. (2012). Clustering using Max-norm Constrained Optimization. International Conference on Machine Learning. 1579–1586.6 indexed citations
14.
Lee, Jason D., Ben Recht, Nathan Srebro, Joel A. Tropp, & Ruslan Salakhutdinov. (2010). Practical Large-Scale Optimization for Max-norm Regularization. CaltechAUTHORS (California Institute of Technology). 23. 1297–1305.79 indexed citations
15.
Sabato, Sivan, Nathan Srebro, & Naftali Tishby. (2010). Reducing Label Complexity by Learning From Bags. International Conference on Artificial Intelligence and Statistics. 685–692.6 indexed citations
Srebro, Nathan, Noga Alon, & Tommi Jaakkola. (2004). Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. Neural Information Processing Systems. 17. 1321–1328.63 indexed citations
18.
Srebro, Nathan, Jason D. M. Rennie, & Tommi Jaakkola. (2004). Maximum-Margin Matrix Factorization. Neural Information Processing Systems. 17. 1329–1336.573 indexed citations breakdown →
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.