Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Taking the Human Out of the Loop: A Review of Bayesian Optimization
20153.4k citationsKevin Swersky, Ryan P. Adams et al.profile →
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).
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 of co-authors of Kevin Swersky
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Swersky.
A scholar is included among the top collaborators of Kevin Swersky based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kevin Swersky. Kevin Swersky is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Vasconcelos, Cristina Nader, Cengiz Öztireli, Mark A. Matthews, et al.. (2023). CUF: Continuous Upsampling Filters. 9999–10008.7 indexed citations
Yan, Yujun, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, & Milad Hashemi. (2020). Neural Execution Engines: Learning to Execute Subroutines. Neural Information Processing Systems. 33. 17298–17308.1 indexed citations
5.
Swersky, Kevin, Yulia Rubanova, David Dohan, & Kevin J. Murphy. (2020). Amortized Bayesian Optimization over Discrete Spaces. Uncertainty in Artificial Intelligence. 769–778.4 indexed citations
Triantafillou, Eleni, Hugo Larochelle, Jake Snell, et al.. (2018). Meta-Learning for Semi-Supervised Few-Shot Classification. arXiv (Cornell University).73 indexed citations
9.
Hashemi, Milad, Kevin Swersky, Jamie Smith, et al.. (2018). Learning Memory Access Patterns. International Conference on Machine Learning. 1919–1928.8 indexed citations
10.
Swersky, Kevin, Jasper Snoek, & Ryan P. Adams. (2013). Multi-Task Bayesian Optimization. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 26. 2004–2012.239 indexed citations
Tarlow, Daniel, Kevin Swersky, Laurent Charlin, Ilya Sutskever, & Rich Zemel. (2013). Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. International Conference on Machine Learning. 199–207.20 indexed citations
13.
Swersky, Kevin, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, & Ryan P. Adams. (2012). Probabilistic n-Choose-k Models for Classification and Ranking. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 25. 3050–3058.7 indexed citations
Swersky, Kevin, Ilya Sutskever, Daniel Tarlow, et al.. (2012). Cardinality Restricted Boltzmann Machines. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 25. 3293–3301.10 indexed citations
16.
Swersky, Kevin, et al.. (2011). On Autoencoders and Score Matching for Energy Based Models. Oxford University Research Archive (ORA) (University of Oxford). 1201–1208.33 indexed citations
17.
Marlin, Benjamin M., Kevin Swersky, Bo Chen, & Nando de Freitas. (2010). Inductive Principles for Restricted Boltzmann Machine Learning. Oxford University Research Archive (ORA) (University of Oxford). 9. 509–516.80 indexed citations
Swersky, Kevin, et al.. (2010). Sparsity priors and boosting for learning localized distributed feature representations.2 indexed citations
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.