Dami Choi
Impact in
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- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Reinforcement Learning in Robotics
- Machine Learning and Algorithms
- Natural Language Processing Techniques
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- Advanced Multi-Objective Optimization Algorithms
- Adaptive Dynamic Programming Control
Papers in
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- Metaheuristic Optimization Algorithms Research 2
- Machine Learning and Data Classification 1
- Stochastic Gradient Optimization Techniques 1
- Domain Adaptation and Few-Shot Learning 1
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- Advanced Multi-Objective Optimization Algorithms 2
- Co-authors
- Niru Maheswaranathan (2 shared papers)George Tucker (2 shared papers)Luke Metz (2 shared papers)Jascha Sohl‐Dickstein (2 shared papers)Daniel Tarlow (1 shared paper)Andreas Krause (1 shared paper)Chris J. Maddison (1 shared paper)Richard Turner (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesCanadaSwitzerland
In The Last Decade
Dami Choi
3 papers receiving 21 citations
Peers
Comparison fields: 5 of 15
- Artificial Intelligence 19
- Computational Theory and Mathematics 6
- Management Science and Operations Research 3
- Numerical Analysis 1
- Catalysis 1
Countries citing papers authored by Dami Choi
This map shows the geographic impact of Dami Choi'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 Dami Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dami Choi more than expected).
Fields of papers citing papers by Dami Choi
This network shows the impact of papers produced by Dami Choi. 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 Dami Choi. The network helps show where Dami Choi may publish in the future.
Co-authors
The 17 scholars most cited alongside Dami Choi, 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 | Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search | 2018 | 10 |
| 2 | 2018 | 9 | |
| 3 | Gradient Estimation with Stochastic Softmax Tricks | 2020 | 3 |
| 4 | 2024 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 0 |
About Dami Choi
Dami Choi is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability and Infectious Diseases, having authored 6 papers that have together received 22 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (2 papers), Metaheuristic Optimization Algorithms Research (2 papers), Machine Learning and Data Classification (1 paper), Statistical Methods and Inference (1 paper), Advanced Bandit Algorithms Research (1 paper), Stochastic Gradient Optimization Techniques (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (19 citations), Computational Theory and Mathematics (6 citations), Management Science and Operations Research (3 citations), Numerical Analysis (1 citation) and Catalysis (1 citation). Dami Choi has collaborated with scholars based in United States, Canada and Switzerland. Frequent co-authors include Niru Maheswaranathan, George Tucker, Luke Metz, Jascha Sohl‐Dickstein, Daniel Tarlow, Andreas Krause, Chris J. Maddison, Richard Turner, David Duvenaud and Behrooz Ghorbani. 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.