Peter Orbanz

877 total citations
12 papers, 314 citations indexed

About

Peter Orbanz is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Peter Orbanz has authored 12 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 4 papers in Statistics and Probability and 1 paper in Molecular Biology. Recurrent topics in Peter Orbanz's work include Bayesian Methods and Mixture Models (10 papers), Gaussian Processes and Bayesian Inference (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Peter Orbanz is often cited by papers focused on Bayesian Methods and Mixture Models (10 papers), Gaussian Processes and Bayesian Inference (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Peter Orbanz collaborates with scholars based in United Kingdom, Switzerland and United States. Peter Orbanz's co-authors include Joachim M. Buhmann, Daniel M. Roy, Zoubin Ghahramani, James Robert Lloyd, Sinead A. Williamson, Ryan P. Adams and Victor Veitch and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The Annals of Statistics and International Journal of Computer Vision.

In The Last Decade

Peter Orbanz

12 papers receiving 288 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Peter Orbanz United Kingdom	6	169	79	77	57	49	12	314
Ambedkar Dukkipati India	10	136 0.8×	100 1.3×	26 0.3×	88 1.5×	9 0.2×	43	337
Warren Schudy United States	8	94 0.6×	27 0.3×	17 0.2×	33 0.6×	42 0.9×	12	228
Zhao Ren United States	10	150 0.9×	16 0.2×	303 3.9×	20 0.4×	57 1.2×	25	529
Zeev Volkovich Israel	9	131 0.8×	24 0.3×	18 0.2×	24 0.4×	24 0.5×	62	297
Rupkumar Mahapatra India	8	58 0.3×	45 0.6×	11 0.1×	32 0.6×	41 0.8×	17	201
Max Buot United States	2	104 0.6×	33 0.4×	22 0.3×	21 0.4×	16 0.3×	3	278
Masamichi Shimura Japan	9	118 0.7×	16 0.2×	18 0.2×	27 0.5×	30 0.6×	28	261
Sushant Sachdeva United States	10	132 0.8×	36 0.5×	33 0.4×	31 0.5×	22 0.4×	32	307
Wang Zhou China	11	203 1.2×	8 0.1×	59 0.8×	50 0.9×	9 0.2×	33	347

Countries citing papers authored by Peter Orbanz

Since Specialization

Citations

This map shows the geographic impact of Peter Orbanz'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 Peter Orbanz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Orbanz more than expected).

Fields of papers citing papers by Peter Orbanz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Orbanz. 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 Peter Orbanz. The network helps show where Peter Orbanz may publish in the future.

Co-authorship network of co-authors of Peter Orbanz

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Orbanz. A scholar is included among the top collaborators of Peter Orbanz 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 Peter Orbanz. Peter Orbanz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

Orbanz, Peter, et al.. (2022). Limit theorems for distributions invariant under groups of transformations. The Annals of Statistics. 50(4). 4 indexed citations

Veitch, Victor, et al.. (2018). Compressibility and Generalization in Large-Scale Deep Learning.. arXiv (Cornell University). 2 indexed citations

Orbanz, Peter & Daniel M. Roy. (2014). Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(2). 437–461. 87 indexed citations

Lloyd, James Robert, Peter Orbanz, Zoubin Ghahramani, & Daniel M. Roy. (2012). Random function priors for exchangeable arrays with applications to graphs and relational data. 25. 998–1006. 44 indexed citations

Orbanz, Peter. (2011). Projective limit random probabilities on Polish spaces. Electronic Journal of Statistics. 5(none). 2 indexed citations

Williamson, Sinead A., Peter Orbanz, & Zoubin Ghahramani. (2010). Dependent Indian Buffet Processes. Cambridge University Engineering Department Publications Database. 9. 924–931. 25 indexed citations

Orbanz, Peter. (2009). Construction of Nonparametric Bayesian Models from Parametric Bayes Equations. Cambridge University Engineering Department Publications Database. 22. 1392–1400. 12 indexed citations

Orbanz, Peter, et al.. (2008). Music preference learning with partial information. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 18. 2021–2024. 2 indexed citations

Orbanz, Peter, et al.. (2007). Cluster analysis of heterogeneous rank data. Repository for Publications and Research Data (ETH Zurich). 113–120. 57 indexed citations

10.

Orbanz, Peter & Joachim M. Buhmann. (2007). Nonparametric Bayesian Image Segmentation. International Journal of Computer Vision. 77(1-3). 25–45. 71 indexed citations

11.

Orbanz, Peter & Joachim M. Buhmann. (2006). Smooth Image Segmentation by Nonparametric Bayesian Inference. Cambridge University Engineering Department Publications Database. 3 indexed citations

12.

Orbanz, Peter & Joachim M. Buhmann. (2005). SAR images as mixtures of Gaussian mixtures. II–209. 5 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.

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