N. Littlestone
Impact in
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- Advanced Bandit Algorithms Research
- Artificial Intelligence top 1%
- Machine Learning and Algorithms
- Machine Learning and Data Classification
- Algorithms and Data Compression
- Data Stream Mining Techniques
- Reinforcement Learning in Robotics
Papers in
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- Machine Learning and Algorithms 13
- Machine Learning and Data Classification 5
- Imbalanced Data Classification Techniques 3
- Algorithms and Data Compression 2
- Domain Adaptation and Few-Shot Learning 2
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- Optimization and Search Problems 5
- Co-authors
- Manfred K. Warmuth (7 shared papers)David Haussler (3 shared papers)Philip M. Long (6 shared papers)David P. Helmbold (3 shared papers)Avrim Blum (1 shared paper)Lisa Hellerstein (1 shared paper)
- Journals
- Information and Computation (4 papers)Computational Complexity (1 paper)Journal of Computer and System Sciences (1 paper)Conference on Learning Theory (1 paper)
- Partner nations
- United StatesSingaporeAustria
In The Last Decade
N. Littlestone
12 papers receiving 1.5k citations
N. Littlestone's Hit Papers
Peers
Comparison fields: 5 of 102
- Management Science and Operations Research 662
- Artificial Intelligence 1.3k
- Computer Networks and Communications 373
- Computational Theory and Mathematics 224
- Computer Science Applications 41
Countries citing papers authored by N. Littlestone
This map shows the geographic impact of N. 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 N. Littlestone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites N. Littlestone more than expected).
Fields of papers citing papers by N. Littlestone
This network shows the impact of papers produced by N. 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 N. Littlestone. The network helps show where N. Littlestone may publish in the future.
Co-authors
The 6 scholars most cited alongside N. Littlestone, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The Weighted Majority Algorithm Hit paper breakdown → | 1994 | 1074 |
| 2 | 1989 | 184 | |
| 3 | Mistake bounds and logarithmic linear-threshold learning algorithms | 1990 | 128 |
| 4 | 1994 | 84 | |
| 5 | 1988 | 64 | |
| 6 | 1995 | 36 | |
| 7 | 1991 | 28 | |
| 8 | 1995 | 25 | |
| 9 | 2000 | 24 | |
| 10 | 1992 | 16 | |
| 11 | 2000 | 3 | |
| 12 | 1988 | 2 | |
| 13 | 1993 | 2 |
About N. Littlestone
N. Littlestone is a scholar working on Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Control and Systems Engineering and Computational Theory and Mathematics, having authored 13 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (13 papers), Optimization and Search Problems (5 papers), Machine Learning and Data Classification (5 papers), Imbalanced Data Classification Techniques (3 papers), Advanced Bandit Algorithms Research (3 papers), Fault Detection and Control Systems (2 papers), Algorithms and Data Compression (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Management Science and Operations Research (662 citations), Artificial Intelligence (1.3k citations), Computer Networks and Communications (373 citations), Computational Theory and Mathematics (224 citations) and Computer Science Applications (41 citations). N. Littlestone has collaborated with scholars based in United States, Singapore and Austria. Frequent co-authors include Manfred K. Warmuth, David Haussler, Philip M. Long, David P. Helmbold, Avrim Blum and Lisa Hellerstein. Their work appears in journals such as Information and Computation, Computational Complexity, Journal of Computer and System Sciences and Conference on Learning Theory.
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.