Dami Choi
Impact in
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- Metaheuristic Optimization Algorithms Research
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Natural Language Processing Techniques
- Topic Modeling
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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)Chris J. Maddison (1 shared paper)Andreas Krause (1 shared paper)Daniel Tarlow (1 shared paper)James Requeima (1 shared paper)
- Journals
- Neural Information Processing Systems (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- CanadaUnited StatesSwitzerland
In The Last Decade
Dami Choi
3 papers receiving 16 citations
Peers
Comparison fields: 5 of 12
- Artificial Intelligence 13
- Computational Theory and Mathematics 5
- Computer Vision and Pattern Recognition 3
- Information Systems and Management 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 15 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 | 7 |
| 2 | 2018 | 6 | |
| 3 | Gradient Estimation with Stochastic Softmax Tricks | 2020 | 3 |
| 4 | 2024 | 0 | |
| 5 | 2024 | 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 5 papers that have together received 16 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 (13 citations), Computational Theory and Mathematics (5 citations), Computer Vision and Pattern Recognition (3 citations), Information Systems and Management (1 citation) and Catalysis (1 citation). Dami Choi has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Niru Maheswaranathan, George Tucker, Luke Metz, Jascha Sohl‐Dickstein, Chris J. Maddison, Andreas Krause, Daniel Tarlow, James Requeima, Roger Grosse and Owain Evans. Their work appears in journals such as Neural Information Processing Systems and arXiv (Cornell University).
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.