Christopher Liaw
Impact in
- Artificial Intelligence top 10%
- Neural Networks and Applications
- Machine Learning and Algorithms
- Stochastic Gradient Optimization Techniques
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
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- Advanced Bandit Algorithms Research
- Auction Theory and Applications
Papers in
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- Machine Learning and Algorithms 6
- Stochastic Gradient Optimization Techniques 3
- Machine Learning and Data Classification 2
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- Auction Theory and Applications 4
- Advanced Bandit Algorithms Research 3
- Co-authors
- Abbas Mehrabian (3 shared papers)Peter L. Bartlett (1 shared paper)Nicholas J. A. Harvey (6 shared papers)Aranyak Mehta (5 shared papers)Weiwei Kong (1 shared paper)D. Sivakumar (1 shared paper)Yaniv Plan (3 shared papers)Paul Liu (1 shared paper)
- Journals
- Journal of the ACM (1 paper)Discrete Mathematics (1 paper)Journal of Machine Learning Research (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Conference on Learning Theory (1 paper)
- Partner nations
- CanadaUnited StatesJapan
In The Last Decade
Christopher Liaw
11 papers receiving 147 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 99
- Management Science and Operations Research 28
- Statistics and Probability 13
- Computer Graphics and Computer-Aided Design 5
- Computer Vision and Pattern Recognition 28
Countries citing papers authored by Christopher Liaw
This map shows the geographic impact of Christopher Liaw'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 Christopher Liaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Liaw more than expected).
Fields of papers citing papers by Christopher Liaw
This network shows the impact of papers produced by Christopher Liaw. 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 Christopher Liaw. The network helps show where Christopher Liaw may publish in the future.
Co-authors
The 18 scholars most cited alongside Christopher Liaw, 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 | 2017 | 93 | |
| 2 | A new dog learns old tricks: RL finds classic optimization algorithms | 2019 | 17 |
| 3 | Tight analyses for non-smooth stochastic gradient descent | 2019 | 11 |
| 4 | 2018 | 10 | |
| 5 | 2021 | 9 | |
| 6 | Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes | 2018 | 5 |
| 7 | 2020 | 3 | |
| 8 | 2023 | 3 | |
| 9 | Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds | 2020 | 2 |
| 10 | 2017 | 1 | |
| 11 | 2024 | 1 | |
| 12 | 2020 | 0 | |
| 13 | 2024 | 0 |
About Christopher Liaw
Christopher Liaw is a scholar working on Artificial Intelligence, Management Science and Operations Research, Marketing, Computer Networks and Communications and Computational Theory and Mathematics, having authored 13 papers that have together received 155 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (6 papers), Auction Theory and Applications (4 papers), Consumer Market Behavior and Pricing (4 papers), Optimization and Search Problems (3 papers), Advanced Bandit Algorithms Research (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Machine Learning and Data Classification (2 papers) and Complexity and Algorithms in Graphs (2 papers). The work is most often cited by research in Artificial Intelligence (99 citations), Management Science and Operations Research (28 citations), Statistics and Probability (13 citations), Computer Graphics and Computer-Aided Design (5 citations) and Computer Vision and Pattern Recognition (28 citations). Christopher Liaw has collaborated with scholars based in Canada, United States and Japan. Frequent co-authors include Abbas Mehrabian, Peter L. Bartlett, Nicholas J. A. Harvey, Aranyak Mehta, Weiwei Kong, D. Sivakumar, Yaniv Plan, Paul Liu, Zhe Feng and Abhishek Sethi. Their work appears in journals such as Journal of the ACM, Discrete Mathematics, Journal of Machine Learning Research, Proceedings of the AAAI Conference on Artificial Intelligence and Conference on Learning Theory.
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.