Nick Littlestone

3.0k total citations · 1 hit paper
16 papers, 1.8k citations indexed

About

Nick Littlestone is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Nick Littlestone has authored 16 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 6 papers in Computational Theory and Mathematics and 4 papers in Management Science and Operations Research. Recurrent topics in Nick Littlestone's work include Machine Learning and Algorithms (16 papers), Algorithms and Data Compression (9 papers) and Machine Learning and Data Classification (8 papers). Nick Littlestone is often cited by papers focused on Machine Learning and Algorithms (16 papers), Algorithms and Data Compression (9 papers) and Machine Learning and Data Classification (8 papers). Nick Littlestone collaborates with scholars based in United States and Italy. Nick Littlestone's co-authors include Manfred K. Warmuth, Michael Kearns, David Haussler, Dale Schuurmans, Adam J. Grove, Claudio Gentile, Lisa Hellerstein, Avrim Blum and Anselm Blumer and has published in prestigious journals such as Machine Learning, Discrete Applied Mathematics and Information and Computation.

In The Last Decade

Nick Littlestone

15 papers receiving 1.6k 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.

Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

1988 850 citations Nick Littlestone Machine Learning profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nick Littlestone United States	12	1.6k	412	295	262	223	16	1.8k
N. Littlestone United States	10	1.2k 0.8×	224 0.5×	658 2.2×	372 1.4×	128 0.6×	13	1.7k
Solomon Eyal Shimony Israel	18	788 0.5×	212 0.5×	124 0.4×	311 1.2×	289 1.3×	83	1.3k
Hans Ulrich Simon Germany	16	620 0.4×	275 0.7×	85 0.3×	215 0.8×	216 1.0×	76	1.0k
Philip Laird United States	10	1.0k 0.7×	237 0.6×	270 0.9×	618 2.4×	137 0.6×	21	1.5k
Peter Sussner Brazil	22	1.1k 0.7×	412 1.0×	328 1.1×	96 0.4×	421 1.9×	80	1.6k
Alina Beygelzimer United States	14	841 0.5×	92 0.2×	165 0.6×	289 1.1×	342 1.5×	36	1.4k
Claudio Gentile Italy	20	1.4k 0.9×	75 0.2×	630 2.1×	228 0.9×	306 1.4×	66	1.8k
Roni Khardon United States	17	723 0.5×	319 0.8×	56 0.2×	185 0.7×	82 0.4×	78	1.1k
Alekh Agarwal United States	21	1.0k 0.7×	85 0.2×	419 1.4×	366 1.4×	248 1.1×	61	1.5k
Shiva Prasad Kasiviswanathan United States	15	1.4k 0.9×	83 0.2×	88 0.3×	140 0.5×	169 0.8×	38	1.6k

Countries citing papers authored by Nick Littlestone

Since Specialization

Citations

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

Fields of papers citing papers by Nick Littlestone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nick Littlestone

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

All Works

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16 of 16 papers shown

1.

Littlestone, Nick & Manfred K. Warmuth. (2003). Relating Data Compression and Learnability. 86 indexed citations

2.

Grove, Adam J., Nick Littlestone, & Dale Schuurmans. (2001). General Convergence Results for Linear Discriminant Updates. Machine Learning. 43(3). 173–210. 57 indexed citations

3.

Gentile, Claudio & Nick Littlestone. (1999). The robustness of the p -norm algorithms. 1–11. 27 indexed citations

4.

Grove, Adam J., Nick Littlestone, & Dale Schuurmans. (1997). General convergence results for linear discriminant updates. 171–183. 32 indexed citations

5.

Littlestone, Nick, et al.. (1996). An Apobayesian Relative of Winnow. Neural Information Processing Systems. 9. 204–210. 14 indexed citations

6.

Blum, Avrim, Lisa Hellerstein, & Nick Littlestone. (1991). Learning in the presence of finitely or infinitely many irrelevant attributes. Conference on Learning Theory. 157–166. 11 indexed citations

7.

Haussler, David, Michael Kearns, Nick Littlestone, & Manfred K. Warmuth. (1991). Equivalence of models for polynomial learnability. Information and Computation. 95(2). 129–161. 82 indexed citations

8.

Littlestone, Nick. (1989). From on-line to batch learning. Conference on Learning Theory. 269–284. 83 indexed citations

9.

Blumer, Anselm & Nick Littlestone. (1989). Learning faster than promised by the Vapnik-Chervonenkis dimension. Discrete Applied Mathematics. 24(1-3). 47–53. 5 indexed citations

10.

Haussler, David, Nick Littlestone, & Manfred K. Warmuth. (1988). Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract). 100–109. 1 indexed citations

11.

Haussler, David, Michael Kearns, Nick Littlestone, & Manfred K. Warmuth. (1988). Equivalence of models for polynomial learnability. Conference on Learning Theory. 42–55. 53 indexed citations

12.

Littlestone, Nick. (1988). Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm. Machine Learning. 2(4). 285–318. 850 indexed citations breakdown →

13.

Littlestone, Nick. (1988). Learning Abound: Quickly When Irrelevant Attributes A New Linear-threshold Algorithm. 5 indexed citations

14.

Littlestone, Nick. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning. 2(4). 285–318. 374 indexed citations

15.

Littlestone, Nick. (1987). Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm Extended Abstract. 68–77. 1 indexed citations

16.

Littlestone, Nick. (1987). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. 68–77. 95 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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