Noah Golowich
About
In The Last Decade
Noah Golowich
10 papers receiving 49 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 27
- Management Science and Operations Research 23
- Marketing 9
- Economics and Econometrics 6
- Computer Networks and Communications 5
Countries citing papers authored by Noah Golowich
This map shows the geographic impact of Noah Golowich'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 Noah Golowich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noah Golowich more than expected).
Fields of papers citing papers by Noah Golowich
This network shows the impact of papers produced by Noah Golowich. 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 Noah Golowich. The network helps show where Noah Golowich may publish in the future.
Co-authorship network of co-authors of Noah Golowich
This figure shows the co-authorship network connecting the top 25 collaborators of Noah Golowich. A scholar is included among the top collaborators of Noah Golowich 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 Noah Golowich. Noah Golowich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 1 | |
| 8 | Tight last-iterate convergence rates for no-regret learning in multi-player games | 1 |
| 9 | A convergence analysis of gradient descent for deep linear neural networks | 11 |
| 10 | 24 | |
| 11 | Musings on Deep Learning: Properties of SGD | 1 |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 0 |
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.