Mark Herbster

2.4k total citations
25 papers, 1.3k citations indexed

About

Mark Herbster is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Science and Operations Research. According to data from OpenAlex, Mark Herbster has authored 25 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 11 papers in Computer Networks and Communications and 7 papers in Management Science and Operations Research. Recurrent topics in Mark Herbster's work include Machine Learning and Algorithms (12 papers), Optimization and Search Problems (9 papers) and Advanced Bandit Algorithms Research (7 papers). Mark Herbster is often cited by papers focused on Machine Learning and Algorithms (12 papers), Optimization and Search Problems (9 papers) and Advanced Bandit Algorithms Research (7 papers). Mark Herbster collaborates with scholars based in United Kingdom, United States and Italy. Mark Herbster's co-authors include Manfred K. Warmuth, Massimiliano Pontil, Simone Severini, Carlo Ciliberto, Andrea Rocchetto, Leonard Wossnig, Shiqiang Wang, Ting He, Andreas A. Argyriou and Peter Auer and has published in prestigious journals such as The Lancet, Machine Learning and Journal of Machine Learning Research.

In The Last Decade

Mark Herbster

22 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Herbster United Kingdom	13	688	291	196	143	125	25	1.3k
Staal A. Vinterbo United States	19	462 0.7×	63 0.2×	52 0.3×	330 2.3×	99 0.8×	41	1.1k
Jan Ramon Belgium	20	608 0.9×	75 0.3×	39 0.2×	312 2.2×	212 1.7×	101	1.4k
Ioannis Kontoyiannis United States	21	620 0.9×	272 0.9×	118 0.6×	182 1.3×	222 1.8×	102	1.5k
Michael U. Gutmann Finland	16	1.1k 1.5×	45 0.2×	79 0.4×	185 1.3×	45 0.4×	51	1.9k
Jiashun Jin United States	23	627 0.9×	77 0.3×	116 0.6×	456 3.2×	27 0.2×	51	1.9k
Shuheng Zhou United States	13	372 0.5×	143 0.5×	39 0.2×	68 0.5×	89 0.7×	41	852
Alessandro Rinaldo United States	20	565 0.8×	60 0.2×	75 0.4×	139 1.0×	203 1.6×	57	1.5k
David J. Marchette United States	20	612 0.9×	307 1.1×	30 0.2×	98 0.7×	64 0.5×	72	1.3k
Aarti Singh United States	18	436 0.6×	225 0.8×	32 0.2×	54 0.4×	153 1.2×	66	947
Paul Stolorz United States	12	334 0.5×	81 0.3×	28 0.1×	198 1.4×	95 0.8×	30	850

Countries citing papers authored by Mark Herbster

Since Specialization

Citations

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

Fields of papers citing papers by Mark Herbster

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Herbster

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

All Works

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20 of 20 papers shown

Herbster, Mark, et al.. (2022). Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm. Machine Learning. 112(1). 241–280. 3 indexed citations

Hajiesmaili, Mohammad, et al.. (2022). Hierarchical Learning Algorithms for Multi-scale Expert Problems. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 6(2). 1–29.

Severini, Simone, et al.. (2018). Image classification with quantum pre-training and auto-encoders. International Journal of Quantum Information. 16(8). 1840009–1840009. 11 indexed citations

Wang, Shiqiang, et al.. (2017). Data distribution and scheduling for distributed analytics tasks. 1–6. 9 indexed citations

Malki, Karim, et al.. (2017). Epigenetic Differences In Monozygotic Twins Discordant For Major Depressive Disorder. European Neuropsychopharmacology. 27. S382–S383. 1 indexed citations

Malki, Karim, et al.. (2016). Epigenetic differences in monozygotic twins discordant for major depressive disorder. Translational Psychiatry. 6(6). e839–e839. 35 indexed citations

Herbster, Mark, et al.. (2016). Mistake Bounds for Binary Matrix Completion. UCL Discovery (University College London). 29. 3954–3962. 3 indexed citations

Malki, Karim, Maria Grazia Tosto, Oliver Pain, et al.. (2016). Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression. American Journal of Medical Genetics Part B Neuropsychiatric Genetics. 174(3). 235–250. 9 indexed citations

Herbster, Mark, et al.. (2015). Predicting a switching sequence of graph labelings. Journal of Machine Learning Research. 16(1). 2003–2022. 2 indexed citations

10.

Herbster, Mark, et al.. (2015). Online prediction at the limit of zero temperature. Neural Information Processing Systems. 28. 2935–2943.

11.

Gentile, Claudio, et al.. (2013). Online Similarity Prediction of Networked Data from Known and Unknown Graphs. arXiv (Cornell University). 662–695. 5 indexed citations

12.

Herbster, Mark. (2010). A Triangle Inequality for p-Resistance. UCL Discovery (University College London). 1 indexed citations

13.

Herbster, Mark & Guy Lever. (2009). Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation.. UCL Discovery (University College London). 23 indexed citations

14.

Herbster, Mark, et al.. (2008). Fast Prediction on a Tree. UCL Discovery (University College London). 21. 657–664. 21 indexed citations

15.

Herbster, Mark, Guy Lever, & Massimiliano Pontil. (2008). Online Prediction on Large Diameter Graphs. UCL Discovery (University College London). 21. 649–656. 18 indexed citations

16.

Agranoff, Dan, Delmiro Fernández-Reyes, Marios C. Papadopoulos, et al.. (2006). Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. The Lancet. 368(9540). 1012–1021. 215 indexed citations

17.

Argyriou, Andreas A., Mark Herbster, & Massimiliano Pontil. (2005). Combining Graph Laplacians for Semi--Supervised Learning. UCL Discovery (University College London). 18. 67–74. 90 indexed citations

18.

Herbster, Mark & Manfred K. Warmuth. (1998). Tracking the best regressor. 24–31. 24 indexed citations

19.

Auer, Peter, Mark Herbster, & Manfred K. Warmuth. (1995). Exponentially many local minima for single neurons. Neural Information Processing Systems. 8. 316–322. 58 indexed citations

20.

Grate, Leslie R., et al.. (1994). RNA modeling using Gibbs sampling and stochastic context free grammars.. PubMed. 2. 138–46. 19 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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