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.
A universal noise removal algorithm with an impulse detector
Citations per year, relative to Roman Garnett Roman Garnett (= 1×)
peers
Ian Buck
Countries citing papers authored by Roman Garnett
Since
Specialization
Citations
This map shows the geographic impact of Roman Garnett'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 Roman Garnett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Garnett more than expected).
This network shows the impact of papers produced by Roman Garnett. 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 Roman Garnett. The network helps show where Roman Garnett may publish in the future.
Co-authorship network of co-authors of Roman Garnett
This figure shows the co-authorship network connecting the top 25 collaborators of Roman Garnett.
A scholar is included among the top collaborators of Roman Garnett 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 Roman Garnett. Roman Garnett is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gardner, Jacob R., Chuan Guo, Kilian Q. Weinberger, Roman Garnett, & Roger Grosse. (2017). Discovering and Exploiting Additive Structure for Bayesian Optimization. International Conference on Artificial Intelligence and Statistics. 1311–1319.29 indexed citations
12.
Garnett, Roman, et al.. (2016). BASC: applying Bayesian optimization to the search for global minima on potential energy surfaces. International Conference on Machine Learning. 898–907.15 indexed citations
Garnett, Roman, et al.. (2016). Bayesian optimization for automated model selection. Neural Information Processing Systems. 29. 2892–2900.27 indexed citations
15.
Ma, Yifei, Danica J. Sutherland, Roman Garnett, & Jeff Schneider. (2015). Active Pointillistic Pattern Search. International Conference on Artificial Intelligence and Statistics. 672–680.2 indexed citations
16.
Ma, Yifei, Roman Garnett, & Jeff Schneider. (2014). Active Area Search via Bayesian Quadrature. International Conference on Artificial Intelligence and Statistics. 595–603.5 indexed citations
17.
Ma, Yifei, Roman Garnett, & Jeff Schneider. (2013). Σ-Optimality for Active Learning on Gaussian Random Fields. Neural Information Processing Systems. 26. 2751–2759.16 indexed citations
18.
Neumann, Marion, Roman Garnett, & Kristian Kersting. (2013). Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels. Asian Conference on Machine Learning. 357–372.4 indexed citations
19.
Osborne, Michael A., Roman Garnett, Stephen Roberts, et al.. (2012). Bayesian Quadrature for Ratios. International Conference on Artificial Intelligence and Statistics. 832–840.8 indexed citations
20.
Osborne, Michael A., Roman Garnett, Zoubin Ghahramani, et al.. (2012). Active Learning of Model Evidence Using Bayesian Quadrature. Cambridge University Engineering Department Publications Database. 25. 46–54.24 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.