Kevin Robert Canini
- Artificial Intelligence top 5%
- Information Systems top 5%
- Sociology and Political Science
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Thomas L. GriffithsLei ShiBongwon SuhPeter PirolliMaya R. GuptaJan PfeiferAndrew CotterArmando Fox
- Topics
- Bayesian Methods and Mixture Models (5 papers)Bayesian Modeling and Causal Inference (3 papers)Language and cultural evolution (3 papers)
- Partner nations
- United StatesCanadaBelgium
In The Last Decade
Kevin Robert Canini
16 papers receiving 390 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 221
- Information Systems 118
- Sociology and Political Science 89
- Computer Vision and Pattern Recognition 65
- Statistical and Nonlinear Physics 59
Countries citing papers authored by Kevin Robert Canini
This map shows the geographic impact of Kevin Robert Canini'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 Robert Canini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Robert Canini more than expected).
Fields of papers citing papers by Kevin Robert Canini
This network shows the impact of papers produced by Kevin Robert Canini. 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 Robert Canini. The network helps show where Kevin Robert Canini may publish in the future.
Co-authorship network of co-authors of Kevin Robert Canini
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Robert Canini. A scholar is included among the top collaborators of Kevin Robert Canini 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 Robert Canini. Kevin Robert Canini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 9 | |
| 3 | Diminishing Returns Shape Constraints for Interpretability and Regularization | 10 |
| 4 | Deep Lattice Networks and Partial Monotonic Functions | 13 |
| 5 | Fast and Flexible Monotonic Functions with Ensembles of Lattices | 10 |
| 6 | 37 | |
| 7 | Launch and Iterate: Reducing Prediction Churn | 11 |
| 8 | 13 | |
| 9 | Segmenting and Recognizing Human Action using Low-level Video Features | 7 |
| 10 | Discovering Inductive Biases in Categorization through Iterated Learning | 2 |
| 11 | 92 | |
| 12 | Nonparametric hierarchical bayesian models of categorization | 1 |
| 13 | 11 | |
| 14 | Modeling transfer learning in human categorization with the hierarchical dirichlet process | 24 |
| 15 | Online Inference of Topics with Latent Dirichlet Allocation | 130 |
| 16 | A Case For Adaptive Datacenters To Conserve Energy and Improve Reliability | 29 |
About Kevin Robert Canini
Kevin Robert Canini is a scholar working on Cultural Studies, Artificial Intelligence and Developmental and Educational Psychology, having authored 16 papers that have together received 413 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (5 papers), Bayesian Modeling and Causal Inference (3 papers) and Language and cultural evolution (3 papers). The work is most often cited by research in Artificial Intelligence (221 citations), Information Systems (118 citations) and Statistical and Nonlinear Physics (59 citations). Kevin Robert Canini has collaborated with scholars based in United States, Canada and Belgium. Frequent co-authors include Thomas L. Griffiths, Lei Shi, Bongwon Suh, Peter Pirolli, Maya R. Gupta, Jan Pfeifer, Andrew Cotter, Armando Fox, Peter Bodík and Michael Armbrust. Their work appears in journals such as Journal of Machine Learning Research, Psychonomic Bulletin & Review and Cognitive Science.
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.