SingerYoram

2.3k total citations · 2 hit papers
8 papers, 1.9k citations indexed

About

SingerYoram is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Science and Operations Research. According to data from OpenAlex, SingerYoram has authored 8 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Management Science and Operations Research. Recurrent topics in SingerYoram's work include Advanced Bandit Algorithms Research (3 papers), Scheduling and Optimization Algorithms (1 paper) and Neural Networks and Applications (1 paper). SingerYoram is often cited by papers focused on Advanced Bandit Algorithms Research (3 papers), Scheduling and Optimization Algorithms (1 paper) and Neural Networks and Applications (1 paper). SingerYoram collaborates with scholars based in . SingerYoram's co-authors include Shalev-ShwartzShai and has published in prestigious journals such as Journal of Machine Learning Research and Mathematical Programming.

In The Last Decade

SingerYoram

8 papers receiving 1.9k 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 →
On the algorithmic implementation of multiclass kernel-based vector machines

2002 739 citations 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
SingerYoram	6	1.2k	644	229	183	167	8	1.9k
宏治津田 Japan	1	1.1k 0.9×	867 1.3×	172 0.8×	165 0.9×	115 0.7×	2	2.0k
Rong Jin United States	20	1.3k 1.1×	819 1.3×	418 1.8×	179 1.0×	176 1.1×	71	2.2k
Rajat Raina United States	10	1.2k 1.0×	823 1.3×	148 0.6×	234 1.3×	172 1.0×	11	2.0k
Yuh‐Jye Lee Taiwan	15	1.0k 0.9×	727 1.1×	152 0.7×	221 1.2×	173 1.0×	44	1.7k
Serhii Havrylov Ukraine	3	1.4k 1.2×	803 1.2×	346 1.5×	229 1.3×	64 0.4×	6	2.4k
Kai Yu Germany	23	1.7k 1.4×	1.1k 1.8×	533 2.3×	207 1.1×	273 1.6×	48	2.8k
Yaoliang Yu Canada	20	1.0k 0.9×	842 1.3×	195 0.9×	118 0.6×	184 1.1×	66	1.8k
Rie Johnson United States	10	1.8k 1.5×	301 0.5×	262 1.1×	113 0.6×	421 2.5×	12	2.3k
Francesco Camastra Italy	19	900 0.8×	779 1.2×	98 0.4×	223 1.2×	68 0.4×	39	1.8k
Jin Huang China	21	1.2k 1.0×	720 1.1×	660 2.9×	270 1.5×	107 0.6×	90	2.3k

Countries citing papers authored by SingerYoram

Since Specialization

Citations

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

Fields of papers citing papers by SingerYoram

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of SingerYoram

This figure shows the co-authorship network connecting the top 25 collaborators of SingerYoram. A scholar is included among the top collaborators of SingerYoram 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 SingerYoram. SingerYoram 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:

8 of 8 papers shown

1.

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

2.

SingerYoram, et al.. (2011). Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research. 249 indexed citations

3.

SingerYoram, et al.. (2009). Efficient Online and Batch Learning Using Forward Backward Splitting. Journal of Machine Learning Research. 162 indexed citations

4.

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

5.

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

6.

Shalev-ShwartzShai, et al.. (2005). Smooth ε-Insensitive Regression by Loss Symmetrization. Journal of Machine Learning Research. 1 indexed citations

7.

SingerYoram, et al.. (2003). A family of additive online algorithms for category ranking. Journal of Machine Learning Research. 5 indexed citations

8.

SingerYoram, et al.. (2002). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research. 739 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.

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