Mark D. Reid
Impact in
- Artificial Intelligence top 10%
- Machine Learning and Algorithms
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
Papers in
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- Advanced Bandit Algorithms Research 5
-
- Statistical Methods and Inference 3
- Co-authors
- Robert C. WilliamsonAllison A. EddyJie ZhouPeng SunRafael FrongilloMichaël BlautGarry J. RucklidgeTibério S. Caetano
- Journals
- Proceedings of The Nutrition Society (2 papers)Machine Learning (2 papers)Journal of Machine Learning Research (2 papers)European Journal of Clinical Nutrition (1 paper)Philosophical Psychology (1 paper)
- Partner nations
- AustraliaUnited KingdomUnited States
In The Last Decade
Mark D. Reid
28 papers receiving 294 citations
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 123
- Computational Mathematics 2
- Management Science and Operations Research 37
- Statistics and Probability 23
- Biochemistry 19
Countries citing papers authored by Mark D. Reid
This map shows the geographic impact of Mark D. Reid'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 Mark D. Reid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark D. Reid more than expected).
Fields of papers citing papers by Mark D. Reid
This network shows the impact of papers produced by Mark D. Reid. 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 Mark D. Reid. The network helps show where Mark D. Reid may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mark D. Reid, 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 | Causal bandits: learning good interventions via causal inference | 2016 | 13 |
| 2 | 2016 | 5 | |
| 3 | 2016 | 1 | |
| 4 | 2015 | 18 | |
| 5 | Convergence analysis of prediction markets via randomized subspace descent | 2015 | 2 |
| 6 | 2014 | 3 | |
| 7 | 2014 | 21 | |
| 8 | Aggregating Predictions via Sequential Mini-Trading | 2013 | 1 |
| 9 | Interpreting prediction markets: a stochastic approach | 2012 | 10 |
| 10 | 2012 | 8 | |
| 11 | Composite Multiclass Losses | 2011 | 22 |
| 12 | 2009 | 62 | |
| 13 | 2009 | 22 | |
| 14 | 2009 | 22 | |
| 15 | 2007 | 3 | |
| 16 | 2005 | 0 | |
| 17 | 2004 | 7 | |
| 18 | Learning to Fly: An Application of Hierarchical Reinforcement Learning | 2000 | 6 |
| 19 | 1973 | 26 | |
| 20 | 1971 | 14 |
About Mark D. Reid
Mark D. Reid is a scholar working on Management Science and Operations Research, Statistics and Probability, Biochemistry, Biological Psychiatry and Artificial Intelligence, having authored 29 papers that have together received 318 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (5 papers), Machine Learning and Algorithms (4 papers), Amino Acid Enzymes and Metabolism (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Statistical Methods and Inference (3 papers), Statistical Mechanics and Entropy (3 papers), Sports Analytics and Performance (3 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Artificial Intelligence (123 citations), Computational Mathematics (2 citations), Management Science and Operations Research (37 citations), Statistics and Probability (23 citations) and Biochemistry (19 citations). Mark D. Reid has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include Robert C. Williamson, Allison A. Eddy, Jie Zhou, Peng Sun, Rafael Frongillo, Michaël Blaut, Garry J. Rucklidge, Tibério S. Caetano, Gabriele Hörmannsperger and Elaina Collie–Duguid. Their work appears in journals such as Proceedings of The Nutrition Society, Machine Learning, Journal of Machine Learning Research, European Journal of Clinical Nutrition and Philosophical Psychology.
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.