David P. Helmbold
- Hardware and Architecture top 2%
- Parallel Computing and Optimization Techniques 16
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- Advanced Bandit Algorithms Research 13
- Artificial Intelligence top 1%
- Machine Learning and Algorithms 35
- Algorithms and Data Compression 14
- Machine Learning and Data Classification 13
- Imbalanced Data Classification Techniques 5
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- Optimization and Search Problems 10
- Distributed systems and fault tolerance 10
- Geology top 2%
- Co-authors
- Manfred K. WarmuthCharles E. McDowellRobert E. SchapirePhilip M. LongNicolò Cesa‐BianchiYoav FreundNigel DuffySuresh K. Lodha
- Partner nations
- United StatesItalyAustria
In The Last Decade
David P. Helmbold
72 papers receiving 2.3k citations
Peers
Comparison fields: 5 of 118
- Hardware and Architecture 435
- Management Science and Operations Research 605
- Artificial Intelligence 1.2k
- Computer Networks and Communications 829
- Geology 172
Countries citing papers authored by David P. Helmbold
This map shows the geographic impact of David P. Helmbold'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 David P. Helmbold with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David P. Helmbold more than expected).
Fields of papers citing papers by David P. Helmbold
This network shows the impact of papers produced by David P. Helmbold. 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 David P. Helmbold. The network helps show where David P. Helmbold may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David P. Helmbold, 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 | 2019 | 0 | |
| 2 | Gradient descent with identity initialization efficiently learns positive definite linear transformations. | 2018 | 3 |
| 3 | 2015 | 7 | |
| 4 | All-Moves-As-First Heuristics in Monte-Carlo Go | 2009 | 21 |
| 5 | 2009 | 31 | |
| 6 | BiBoost for Asymmetric Learning | 2005 | 2 |
| 7 | 2001 | 3 | |
| 8 | 2001 | 3 | |
| 9 | Leveraging for Regression | 2000 | 18 |
| 10 | 2000 | 25 | |
| 11 | Potential Boosters | 1999 | 27 |
| 12 | 1999 | 31 | |
| 13 | Worst-case Loss Bounds for Single Neurons | 1995 | 7 |
| 14 | 1995 | 42 | |
| 15 | A CLASS OF SYNCHRONIZATION OPERATIONS THAT PERMIT EFFICIENT RACE DETECTION | 1993 | 3 |
| 16 | 1991 | 1 | |
| 17 | 1991 | 17 | |
| 18 | Modeling Speedup greater than n. | 1989 | 17 |
| 19 | ANALYZING TRACES WITH ANONYMOUS SYNCHRONIZATION | 1989 | 22 |
| 20 | Perfect Graphs and Parallel Algorithms. | 1986 | 15 |
About David P. Helmbold
David P. Helmbold is a scholar working on Hardware and Architecture, Artificial Intelligence and Management Science and Operations Research, having authored 73 papers that have together received 2.5k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (35 papers), Parallel Computing and Optimization Techniques (16 papers), Algorithms and Data Compression (14 papers), Machine Learning and Data Classification (13 papers), Advanced Bandit Algorithms Research (13 papers), Optimization and Search Problems (10 papers), Distributed systems and fault tolerance (10 papers) and Imbalanced Data Classification Techniques (5 papers). The work is most often cited by research in Hardware and Architecture (435 citations), Management Science and Operations Research (605 citations) and Artificial Intelligence (1.2k citations). David P. Helmbold has collaborated with scholars based in United States, Italy and Austria. Frequent co-authors include Manfred K. Warmuth, Charles E. McDowell, Robert E. Schapire, Philip M. Long, Nicolò Cesa‐Bianchi, Yoav Freund, Nigel Duffy, Suresh K. Lodha, Darrell D. E. Long and David Haussler. Their work appears in journals such as ACM Computing Surveys, Journal of the ACM and Machine Learning.
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.