Shalev-ShwartzShai

1.0k total citations · 1 hit paper
10 papers, 844 citations indexed

About

Shalev-ShwartzShai is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Shalev-ShwartzShai has authored 10 papers receiving a total of 844 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 2 papers in Control and Systems Engineering. Recurrent topics in Shalev-ShwartzShai's work include Neural Networks and Applications (3 papers), Machine Learning and Algorithms (2 papers) and Machine Learning and ELM (2 papers). Shalev-ShwartzShai is often cited by papers focused on Neural Networks and Applications (3 papers), Machine Learning and Algorithms (2 papers) and Machine Learning and ELM (2 papers). Shalev-ShwartzShai collaborates with scholars based in . Shalev-ShwartzShai's co-authors include SingerYoram and has published in prestigious journals such as Journal of Machine Learning Research and Mathematical Programming.

In The Last Decade

Shalev-ShwartzShai

8 papers receiving 826 citations

Hit Papers

align trajectories

What are hit papers?

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.

Online Passive-Aggressive Algorithms

2006 744 citations Shalev-ShwartzShai, SingerYoram et al. Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Shalev-ShwartzShai	6	573	222	99	98	84	10	844
Hong-Jie Xing China	13	391 0.7×	169 0.8×	56 0.6×	62 0.6×	74 0.9×	42	640
Linbo Qiao China	14	430 0.8×	387 1.7×	141 1.4×	41 0.4×	44 0.5×	57	911
Mehrdad Farajtabar United States	19	368 0.6×	199 0.9×	107 1.1×	46 0.5×	65 0.8×	37	860
Simon Lacoste-Julien Canada	12	850 1.5×	430 1.9×	64 0.6×	44 0.4×	59 0.7×	33	1.2k
Jinyuan Jia China	15	728 1.3×	247 1.1×	111 1.1×	23 0.2×	105 1.3×	49	1.0k
Chun-Ru Dong China	8	394 0.7×	164 0.7×	101 1.0×	99 1.0×	56 0.7×	31	644
Mingrui Wu United States	14	541 0.9×	526 2.4×	109 1.1×	24 0.2×	76 0.9×	24	917
Chao-Kai Chiang United States	4	325 0.6×	190 0.9×	48 0.5×	61 0.6×	49 0.6×	6	564
Mingming Sun China	15	373 0.7×	388 1.7×	141 1.4×	29 0.3×	44 0.5×	67	787

Countries citing papers authored by Shalev-ShwartzShai

Since Specialization

Citations

This map shows the geographic impact of Shalev-ShwartzShai'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 Shalev-ShwartzShai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shalev-ShwartzShai more than expected).

Fields of papers citing papers by Shalev-ShwartzShai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shalev-ShwartzShai. 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 Shalev-ShwartzShai. The network helps show where Shalev-ShwartzShai may publish in the future.

Co-authorship network of co-authors of Shalev-ShwartzShai

This figure shows the co-authorship network connecting the top 25 collaborators of Shalev-ShwartzShai. A scholar is included among the top collaborators of Shalev-ShwartzShai 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 Shalev-ShwartzShai. Shalev-ShwartzShai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Shalev-ShwartzShai, et al.. (2016). On lower and upper bounds in smooth and strongly convex optimization. Journal of Machine Learning Research.

2.

Shalev-ShwartzShai, et al.. (2015). Multiclass learnability and the ERM principle. Journal of Machine Learning Research.

3.

Shalev-ShwartzShai, et al.. (2013). Efficient active learning of halfspaces. Journal of Machine Learning Research. 1 indexed citations

4.

Shalev-ShwartzShai, et al.. (2013). Stochastic dual coordinate ascent methods for regularized loss. Journal of Machine Learning Research. 8 indexed citations

5.

Shalev-ShwartzShai, et al.. (2011). Pegasos: primal estimated sub-gradient solver for SVM. Mathematical Programming. 7 indexed citations

6.

Shalev-ShwartzShai, et al.. (2011). Stochastic Methods for l1-regularized Loss Minimization. Journal of Machine Learning Research. 58 indexed citations

7.

Shalev-ShwartzShai, et al.. (2010). Learnability, Stability and Uniform Convergence. Journal of Machine Learning Research. 18 indexed citations

8.

Shalev-ShwartzShai, et al.. (2006). Online Passive-Aggressive Algorithms. Journal of Machine Learning Research. 744 indexed citations breakdown →

9.

Shalev-ShwartzShai & SingerYoram. (2006). Efficient Learning of Label Ranking by Soft Projections onto Polyhedra. Journal of Machine Learning Research. 7 indexed citations

10.

Shalev-ShwartzShai, et al.. (2005). Smooth ε-Insensitive Regression by Loss Symmetrization. Journal of Machine Learning Research. 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.

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