Daniel Golovin

5.1k total citations · 3 hit papers
32 papers, 1.9k citations indexed

About

Daniel Golovin is a scholar working on Management Science and Operations Research, Computer Networks and Communications and Computational Theory and Mathematics. According to data from OpenAlex, Daniel Golovin has authored 32 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Management Science and Operations Research, 14 papers in Computer Networks and Communications and 12 papers in Computational Theory and Mathematics. Recurrent topics in Daniel Golovin's work include Optimization and Search Problems (10 papers), Complexity and Algorithms in Graphs (10 papers) and Auction Theory and Applications (8 papers). Daniel Golovin is often cited by papers focused on Optimization and Search Problems (10 papers), Complexity and Algorithms in Graphs (10 papers) and Auction Theory and Applications (8 papers). Daniel Golovin collaborates with scholars based in United States, Switzerland and United Kingdom. Daniel Golovin's 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 and has published in prestigious journals such as Mathematical Programming, AI Magazine and ACM Transactions on Algorithms.

In The Last Decade

Daniel Golovin

32 papers receiving 1.7k citations

Hit Papers

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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.

  1. Ad click prediction
    2013 509 citations Gary D. Holt, D. Sculley et al. profile →
  2. Hidden technical debt in Machine learning systems
    2015 450 citations D. Sculley, Gary D. Holt et al. Neural Information Processing Systems profile →
  3. Google Vizier
    2017 286 citations Daniel Golovin, Greg Kochanski et al. profile →

Peers

Daniel Golovin
Cuiping Li China
Gongqing Wu China
Boi Faltings Switzerland
A.C.M. Fong New Zealand
Xiaoyong Du China
Sergio Ramírez‐Gallego Spain
Yucong Duan China
Αλέξανδρος Νανόπουλος Germany
Martin Zinkevich United States
Rajarshi Das United States
Cuiping Li China
Daniel Golovin
Citations per year, relative to Daniel Golovin Daniel Golovin (= 1×) peers Cuiping Li

Countries citing papers authored by Daniel Golovin

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Golovin, Daniel, et al.. (2021). Forecasting the derivatives market based on a recurrent neural network. 62–68. 1 indexed citations
2.
Golovin, Daniel, et al.. (2020). Gradientless Descent: High-Dimensional Zeroth-Order Optimization. arXiv (Cornell University). 7 indexed citations
3.
Zhang, Richard & Daniel Golovin. (2020). Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. International Conference on Machine Learning. 1. 11096–11105. 4 indexed citations
4.
Golovin, Daniel. (2018). Max-min fair allocation of indivisible goods. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 13 indexed citations
5.
Sculley, D., Gary D. Holt, Daniel Golovin, et al.. (2015). Hidden technical debt in Machine learning systems. Neural Information Processing Systems. 28. 2503–2511. 450 indexed citations breakdown →
6.
Krause, Andreas, Daniel Golovin, & Sarah J. Converse. (2014). Sequential Decision Making in Computational Sustainability Through Adaptive Submodularity. AI Magazine. 35(2). 8–18. 7 indexed citations
7.
Sculley, D., Gary D. Holt, Daniel Golovin, et al.. (2014). Machine Learning: The High Interest Credit Card of Technical Debt. 125 indexed citations
8.
Golovin, Daniel, et al.. (2014). Improved approximations for two-stage min-cut and shortest path problems under uncertainty. Mathematical Programming. 149(1-2). 167–194. 7 indexed citations
9.
Krause, Andreas, et al.. (2011). Randomized sensing in adversarial environments. International Joint Conference on Artificial Intelligence. 2133–2139. 11 indexed citations
10.
Golovin, Daniel & Andreas Krause. (2011). Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. CaltechAUTHORS (California Institute of Technology). 204 indexed citations
11.
Echenique, Federico, Daniel Golovin, & Adam Wierman. (2011). A revealed preference approach to computational complexity in economics. 101–110. 9 indexed citations
12.
Golovin, Daniel, et al.. (2011). Dynamic Resource Allocation in Conservation Planning. Proceedings of the AAAI Conference on Artificial Intelligence. 25(1). 1331–1336. 22 indexed citations
13.
Echenique, Federico, Daniel Golovin, & Adam Wierman. (2011). Complexity and economics. ACM SIGecom Exchanges. 10(1). 2–5. 2 indexed citations
14.
Golovin, Daniel & Andreas Krause. (2010). Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization. arXiv (Cornell University). 24 indexed citations
15.
Streeter, M. J. V., Daniel Golovin, & Andreas Krause. (2009). Online Learning of Assignments. CaltechAUTHORS (California Institute of Technology). 22. 1794–1802. 28 indexed citations
16.
Blelloch, Guy E. & Daniel Golovin. (2008). Uniquely represented data structures with applications to privacy. 7 indexed citations
17.
Streeter, M. J. V., Daniel Golovin, & Stephen F. Smith. (2007). Combining multiple heuristics online. National Conference on Artificial Intelligence. 1197–1203. 35 indexed citations
18.
Streeter, M. J. V., Daniel Golovin, & Stephen F. Smith. (2007). Restart schedules for ensembles of problem instances. National Conference on Artificial Intelligence. 1204–1210. 7 indexed citations
19.
Golovin, Daniel. (2007). Stochastic packing-market planning. 172–181. 3 indexed citations
20.
Golovin, Daniel, Viswanath Nagarajan, & Mohit Singh. (2006). Approximating the k-multicut problem. Symposium on Discrete Algorithms. 621–630. 16 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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