Nicholay Topin
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Computational Theory and Mathematics
- Electrical and Electronic Engineering
- Co-authors
- Manuela VelosoStephanie MilaniFei FangWilliam H. GussPhillip WangRuslan SalakhutdinovJames MacGlashanJohn Winder
- Topics
- Reinforcement Learning in Robotics (7 papers)Adversarial Robustness in Machine Learning (3 papers)Robot Manipulation and Learning (2 papers)
- Journals
- ACM Computing SurveysarXiv (Cornell University)National Conference on Artificial Intelligence
- Partner nations
- United StatesTürkiye
In The Last Decade
Nicholay Topin
9 papers receiving 136 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 88
- Computer Vision and Pattern Recognition 24
- Control and Systems Engineering 16
- Computational Theory and Mathematics 13
- Electrical and Electronic Engineering 11
Countries citing papers authored by Nicholay Topin
This map shows the geographic impact of Nicholay Topin'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 Nicholay Topin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholay Topin more than expected).
Fields of papers citing papers by Nicholay Topin
This network shows the impact of papers produced by Nicholay Topin. 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 Nicholay Topin. The network helps show where Nicholay Topin may publish in the future.
Co-authorship network of co-authors of Nicholay Topin
This figure shows the co-authorship network connecting the top 25 collaborators of Nicholay Topin. A scholar is included among the top collaborators of Nicholay Topin 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 Nicholay Topin. Nicholay Topin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 67 | |
| 2 | 8 | |
| 3 | 2 | |
| 4 | The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective | 3 |
| 5 | 5 | |
| 6 | Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning | 1 |
| 7 | 41 | |
| 8 | Portable option discovery for automated learning transfer in object-oriented Markov decision processes | 15 |
| 9 | Discovering Subgoals in Complex Domains. | 1 |
About Nicholay Topin
Nicholay Topin is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering, having authored 9 papers that have together received 143 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Adversarial Robustness in Machine Learning (3 papers) and Robot Manipulation and Learning (2 papers). The work is most often cited by research in Artificial Intelligence (88 citations), Computer Vision and Pattern Recognition (24 citations) and Software (4 citations). Nicholay Topin has collaborated with scholars based in United States and Türkiye. Frequent co-authors include Manuela Veloso, Stephanie Milani, Fei Fang, William H. Guss, Phillip Wang, Ruslan Salakhutdinov, James MacGlashan, John Winder, Marie desJardins and Daniel J. Stilwell. Their work appears in journals such as ACM Computing Surveys, arXiv (Cornell University) and National Conference on Artificial Intelligence.
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.