Samuel Daulton
Impact in
-
- Advanced Multi-Objective Optimization Algorithms
-
- Probabilistic and Robust Engineering Design
Papers in
-
- Machine Learning and Algorithms 2
- Machine Learning and Data Classification 2
- Gaussian Processes and Bayesian Inference 1
-
- Advanced Bandit Algorithms Research 3
- Co-authors
- Brian Karrer (2 shared papers)Eytan Bakshy (3 shared papers)Maximilian Balandat (3 shared papers)Daniel Jiang (2 shared papers)Benjamin Letham (2 shared papers)Andrew Gordon Wilson (2 shared papers)
- Journals
- arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- IsraelUnited States
In The Last Decade
Samuel Daulton
2 papers receiving 114 citations
Peers
Comparison fields: 5 of 63
- Computational Theory and Mathematics 41
- Statistics, Probability and Uncertainty 10
- Artificial Intelligence 45
- Management Science and Operations Research 16
- Modeling and Simulation 3
Countries citing papers authored by Samuel Daulton
This map shows the geographic impact of Samuel Daulton'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 Samuel Daulton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Daulton more than expected).
Fields of papers citing papers by Samuel Daulton
This network shows the impact of papers produced by Samuel Daulton. 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 Samuel Daulton. The network helps show where Samuel Daulton may publish in the future.
Co-authors
The 6 scholars most cited alongside Samuel Daulton, 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 | 84 | |
| 2 | BoTorch: Programmable Bayesian Optimization in PyTorch. | 2019 | 32 |
| 3 | Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization | 2020 | 0 |
About Samuel Daulton
Samuel Daulton is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 116 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (3 papers), Machine Learning and Algorithms (2 papers), Machine Learning and Data Classification (2 papers), Advanced Multi-Objective Optimization Algorithms (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Computational Theory and Mathematics (41 citations), Statistics, Probability and Uncertainty (10 citations), Artificial Intelligence (45 citations), Management Science and Operations Research (16 citations) and Modeling and Simulation (3 citations). Samuel Daulton has collaborated with scholars based in Israel and United States. Frequent co-authors include Brian Karrer, Eytan Bakshy, Maximilian Balandat, Daniel Jiang, Benjamin Letham and Andrew Gordon Wilson. Their work appears in journals such as arXiv (Cornell University) and Neural Information Processing Systems.
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.