This map shows the geographic impact of Ata Kabán'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 Ata Kabán with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ata Kabán more than expected).
This network shows the impact of papers produced by Ata Kabán. 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 Ata Kabán. The network helps show where Ata Kabán may publish in the future.
Co-authorship network of co-authors of Ata Kabán
This figure shows the co-authorship network connecting the top 25 collaborators of Ata Kabán.
A scholar is included among the top collaborators of Ata Kabán 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 Ata Kabán. Ata Kabán is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kabán, Ata, et al.. (2020). Optimistic bounds for multi-output learning. International Conference on Machine Learning. 1. 8030–8040.2 indexed citations
4.
Kabán, Ata. (2017). On Compressive Ensemble Induced Regularisation:: How Close is the Finite Ensemble Precision Matrix to the Infinite Ensemble?. 617–628.1 indexed citations
5.
Kabán, Ata. (2015). Non-asymptotic Analysis of Compressive Fisher Discriminants in terms of the Effective Dimension. Asian Conference on Machine Learning. 17–32.1 indexed citations
6.
Kabán, Ata. (2015). A New Look at Nearest Neighbours: Identifying Benign Input Geometries via Random Projections. Asian Conference on Machine Learning. 65–80.3 indexed citations
7.
Frénay, Benoît & Ata Kabán. (2014). A Comprehensive Introduction to Label Noise. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)).57 indexed citations
8.
Kabán, Ata. (2014). New Bounds on Compressive Linear Least Squares Regression. University of Birmingham Research Portal (University of Birmingham). 448–456.21 indexed citations
Durrant, Robert J. & Ata Kabán. (2013). Sharp Generalization Error Bounds for Randomly-projected Classifiers. Research Commons (University of Waikato). 693–701.14 indexed citations
11.
Durrant, Robert J. & Ata Kabán. (2013). Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. Research Commons (University of Waikato). 17–32.10 indexed citations
12.
Durrant, Robert J. & Ata Kabán. (2012). Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space. International Conference on Artificial Intelligence and Statistics. 337–345.5 indexed citations
Bootkrajang, Jakramate & Ata Kabán. (2011). Multi-class classification in the presence of labelling errors.. University of Birmingham Research Portal (University of Birmingham).11 indexed citations
Girolami, Mark & Ata Kabán. (2003). Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. UCL Discovery (University College London). 16. 9–16.24 indexed citations
20.
Kabán, Ata. (2001). Latent variable models with application to text based document representation. OpenGrey (Institut de l'Information Scientifique et Technique).1 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.