Daniel M. Kane

76 papers receiving 782 citations

Peers

Daniel M. Kane
Comparison fields: 5 of 77
  • Computational Mathematics 18
  • Computational Theory and Mathematics 278
  • Discrete Mathematics and Combinatorics 51
  • Artificial Intelligence 480
  • Statistics and Probability 112
Replace Raghu Meka with:
Raghu Meka United States
Ravindran Kannan United States
Yin Tat Lee United States
Thore Husfeldt Denmark
Peter Bro Miltersen Denmark
Rocco A. Servedio United States
Ilias Diakonikolas United States
Pascal Koiran France
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Citations per year

Countries citing papers authored by Daniel M. Kane

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Kane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel M. Kane, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel M. Kane Line = papers co-authored together Daniel M. Kane links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010146
2 2014102
3
Proceedings of the 29th Annual Conference on Learning Theory (COLT 2016)
201661
4 201043
5 200542
6 201736
7 201134
8 201527
9 201022
10 202319
11 200817
12
Sever: A Robust Meta-Algorithm for Stochastic Optimization
201817
13 201017
14 201117
15 201615
16 201114
17 201213
18 201412
19 201211
20 201811

About Daniel M. Kane

Daniel M. Kane is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computational Mechanics, Discrete Mathematics and Combinatorics and Statistics and Probability, having authored 90 papers that have together received 863 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (30 papers), Complexity and Algorithms in Graphs (23 papers), Algorithms and Data Compression (11 papers), Sparse and Compressive Sensing Techniques (10 papers), Cryptography and Data Security (8 papers), Analytic Number Theory Research (7 papers), Spatial Cognition and Navigation (6 papers) and Coding theory and cryptography (5 papers). The work is most often cited by research in Computational Mathematics (18 citations), Computational Theory and Mathematics (278 citations), Discrete Mathematics and Combinatorics (51 citations), Artificial Intelligence (480 citations) and Statistics and Probability (112 citations). Daniel M. Kane has collaborated with scholars based in United States, United Kingdom and Hong Kong. Frequent co-authors include Jelani Nelson, Ilias Diakonikolas, David P. Woodruff, Alistair Stewart, Paul Valiant, Joseph Jaeger, Raghu Meka, Ely Porat, Mihir Bellare and Ryan Williams. Their work appears in journals such as Research in the Mathematical Sciences, The Electronic Journal of Combinatorics, Computational Complexity, SIAM Journal on Computing and The Annals of Probability.

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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