Daniel Golovin
- Artificial Intelligence top 1%
- Information Systems top 1%
- Computer Networks and Communications top 2%
- Management Science and Operations Research top 2%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Andreas KrauseD. SculleyTodd PhillipsEugene DavydovGary D. HoltMichael YoungDietmar EbnerGreg Kochanski
- Topics
- Optimization and Search Problems (10 papers)Complexity and Algorithms in Graphs (10 papers)Auction Theory and Applications (8 papers)
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Daniel Golovin
32 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Artificial Intelligence 946
- Information Systems 495
- Computer Networks and Communications 481
- Management Science and Operations Research 348
- Computer Vision and Pattern Recognition 311
Countries citing papers authored by Daniel Golovin
This map shows the geographic impact of Daniel Golovin'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 Golovin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Golovin more than expected).
Fields of papers citing papers by Daniel Golovin
This network shows the impact of papers produced by Daniel Golovin. 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 Golovin. The network helps show where Daniel Golovin may publish in the future.
Co-authorship network of co-authors of Daniel Golovin
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Golovin. A scholar is included among the top collaborators of Daniel Golovin 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 Daniel Golovin. Daniel Golovin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Gradientless Descent: High-Dimensional Zeroth-Order Optimization | 7 |
| 3 | Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization | 4 |
| 4 | 13 | |
| 5 | Hidden technical debt in Machine learning systemsbreakdown → | 450 |
| 6 | 7 | |
| 7 | Machine Learning: The High Interest Credit Card of Technical Debt | 125 |
| 8 | 7 | |
| 9 | 11 | |
| 10 | 204 | |
| 11 | 9 | |
| 12 | 22 | |
| 13 | 2 | |
| 14 | Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization | 24 |
| 15 | Online Learning of Assignments | 28 |
| 16 | Uniquely represented data structures with applications to privacy | 7 |
| 17 | Combining multiple heuristics online | 35 |
| 18 | Restart schedules for ensembles of problem instances | 7 |
| 19 | 3 | |
| 20 | 16 |
About Daniel Golovin
Daniel Golovin is a scholar working on Management Science and Operations Research, Computational Theory and Mathematics and Computer Networks and Communications, having authored 32 papers that have together received 1.9k indexed citations. Recurring topics across this work include Optimization and Search Problems (10 papers), Complexity and Algorithms in Graphs (10 papers) and Auction Theory and Applications (8 papers). The work is most often cited by research in Artificial Intelligence (946 citations), Management Science and Operations Research (348 citations) and Health Informatics (35 citations). Daniel Golovin has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Andreas Krause, D. Sculley, Todd Phillips, Eugene Davydov, Gary D. Holt, Michael Young, Dietmar Ebner, Greg Kochanski, John Karro and Subhodeep Moitra. Their work appears in journals such as Mathematical Programming, AI Magazine and ACM Transactions on Algorithms.
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.