Kevin Swersky
Impact in
- Artificial Intelligence top 0.5%
- Machine Learning and Data Classification
- Gaussian Processes and Bayesian Inference
- Machine Learning and Algorithms
- Domain Adaptation and Few-Shot Learning
- 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
- Gaussian Processes and Bayesian Inference 3
- Neural Networks and Applications 3
- Stochastic Gradient Optimization Techniques 3
-
- Generative Adversarial Networks and Image Synthesis 6
- Co-authors
- Ryan P. AdamsNando de FreitasBobak ShahriariZiyu WangRich ZemelJasper SnoekCynthia DworkYu Wu
- Journals
- Proceedings of the IEEE (1 paper)Journal of Machine Learning Research (1 paper)Oxford University Research Archive (ORA) (University of Oxford) (2 papers)arXiv (Cornell University) (7 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Kevin Swersky
26 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 191
- Artificial Intelligence 1.9k
- Computational Theory and Mathematics 949
- Management Science and Operations Research 454
- Safety Research 263
- Health Informatics 40
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 7 | |
| 2 | 2022 | 92 | |
| 3 | 2021 | 1 | |
| 4 | Neural Execution Engines: Learning to Execute Subroutines | 2020 | 1 |
| 5 | Amortized Bayesian Optimization over Discrete Spaces | 2020 | 4 |
| 6 | 2020 | 6 | |
| 7 | Graph Normalizing Flows | 2019 | 9 |
| 8 | Meta-Learning for Semi-Supervised Few-Shot Classification | 2018 | 73 |
| 9 | Learning Memory Access Patterns | 2018 | 8 |
| 10 | Multi-Task Bayesian Optimization | 2013 | 239 |
| 11 | Learning Fair Representations Hit paper breakdown → | 2013 | 398 |
| 12 | Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning | 2013 | 20 |
| 13 | Probabilistic n-Choose-k Models for Classification and Ranking | 2012 | 7 |
| 14 | 2012 | 23 | |
| 15 | Cardinality Restricted Boltzmann Machines | 2012 | 10 |
| 16 | On Autoencoders and Score Matching for Energy Based Models | 2011 | 33 |
| 17 | Inductive Principles for Restricted Boltzmann Machine Learning | 2010 | 80 |
| 18 | 2010 | 7 | |
| 19 | 2010 | 41 | |
| 20 | Sparsity priors and boosting for learning localized distributed feature representations | 2010 | 2 |
About Kevin Swersky
Kevin Swersky is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Computational Theory and Mathematics and Statistical and Nonlinear Physics, having authored 27 papers that have together received 4.6k 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), Gaussian Processes and Bayesian Inference (3 papers), Neural Networks and Applications (3 papers) and Stochastic Gradient Optimization Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (1.9k citations), Computational Theory and Mathematics (949 citations), Management Science and Operations Research (454 citations), Safety Research (263 citations) and Health Informatics (40 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, Bobak Shahriari, Ziyu Wang, Rich Zemel, Jasper Snoek, Cynthia Dwork, Yu Wu, Richard S. Zemel and Benjamin M. Marlin. Their work appears in journals such as Proceedings of the IEEE, Journal of Machine Learning Research, Oxford University Research Archive (ORA) (University of Oxford), arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.