Kevin Swersky
Impact in
- Artificial Intelligence top 0.5%
- Machine Learning and Data Classification
- Gaussian Processes and Bayesian Inference
- Machine Learning and Algorithms
- Metaheuristic Optimization Algorithms Research
- Computational Theory and Mathematics top 0.5%
- Advanced Multi-Objective Optimization Algorithms
Papers in
-
- Machine Learning and Data Classification 6
- Explainable Artificial Intelligence (XAI) 5
- Machine Learning and Algorithms 3
- Stochastic Gradient Optimization Techniques 3
- Neural Networks and Applications 3
- Gaussian Processes and Bayesian Inference 3
-
- Generative Adversarial Networks and Image Synthesis 6
- Co-authors
- Ryan P. Adams (6 shared papers)Nando de Freitas (6 shared papers)Ziyu Wang (1 shared paper)Bobak Shahriari (1 shared paper)Rich Zemel (3 shared papers)Jasper Snoek (2 shared papers)Cynthia Dwork (1 shared paper)Yu Wu (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Proceedings of the IEEE (1 paper)cIRcle (University of British Columbia) (1 paper)Uncertainty in Artificial Intelligence (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Kevin Swersky
27 papers receiving 5.0k citations
Kevin Swersky's Hit Papers
Peers
Comparison fields: 5 of 187
- Artificial Intelligence 2.2k
- Computational Theory and Mathematics 1.0k
- Management Science and Operations Research 502
- Safety Research 303
- Health Informatics 44
Countries citing papers authored by Kevin Swersky
This map shows the geographic impact of Kevin Swersky'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 Kevin Swersky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Swersky more than expected).
Fields of papers citing papers by Kevin Swersky
This network shows the impact of papers produced by Kevin Swersky. 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 Kevin Swersky. The network helps show where Kevin Swersky may publish in the future.
Co-authors
The 25 scholars most cited alongside Kevin Swersky, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Taking the Human Out of the Loop: A Review of Bayesian Optimization Hit paper breakdown → | 2015 | 3713 |
| 2 | Learning Fair Representations Hit paper breakdown → | 2013 | 451 |
| 3 | Multi-Task Bayesian Optimization | 2013 | 254 |
| 4 | 2022 | 101 | |
| 5 | Inductive Principles for Restricted Boltzmann Machine Learning | 2010 | 92 |
| 6 | The Variational Fair Autoencoder | 2016 | 83 |
| 7 | Meta-Learning for Semi-Supervised Few-Shot Classification | 2018 | 77 |
| 8 | 2019 | 59 | |
| 9 | 2014 | 51 | |
| 10 | 2021 | 50 | |
| 11 | 2010 | 49 | |
| 12 | On Autoencoders and Score Matching for Energy Based Models | 2011 | 39 |
| 13 | 2012 | 24 | |
| 14 | Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning | 2013 | 21 |
| 15 | 2012 | 11 | |
| 16 | Cardinality Restricted Boltzmann Machines | 2012 | 11 |
| 17 | Graph Normalizing Flows | 2019 | 9 |
| 18 | Learning Memory Access Patterns | 2018 | 8 |
| 19 | 2023 | 8 | |
| 20 | Probabilistic n-Choose-k Models for Classification and Ranking | 2012 | 7 |
About Kevin Swersky
Kevin Swersky is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 27 papers that have together received 5.1k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (6 papers), Machine Learning and Data Classification (6 papers), Explainable Artificial Intelligence (XAI) (5 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Machine Learning and Algorithms (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Neural Networks and Applications (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). The work is most often cited by research in Artificial Intelligence (2.2k citations), Computational Theory and Mathematics (1.0k citations), Management Science and Operations Research (502 citations), Safety Research (303 citations) and Health Informatics (44 citations). Kevin Swersky has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Ryan P. Adams, Nando de Freitas, Ziyu Wang, Bobak Shahriari, Rich Zemel, Jasper Snoek, Cynthia Dwork, Yu Wu, Benjamin M. Marlin and Richard S. Zemel. Their work appears in journals such as Journal of Machine Learning Research, Proceedings of the IEEE, cIRcle (University of British Columbia), Uncertainty in Artificial Intelligence 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.