Ricardo Vilalta

3.6k total citations · 1 hit paper
56 papers, 2.1k citations indexed

About

Ricardo Vilalta is a scholar working on Artificial Intelligence, Astronomy and Astrophysics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ricardo Vilalta has authored 56 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Astronomy and Astrophysics and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ricardo Vilalta's work include Machine Learning and Data Classification (8 papers), Machine Learning and Algorithms (7 papers) and Data Mining Algorithms and Applications (5 papers). Ricardo Vilalta is often cited by papers focused on Machine Learning and Data Classification (8 papers), Machine Learning and Algorithms (7 papers) and Data Mining Algorithms and Applications (5 papers). Ricardo Vilalta collaborates with scholars based in United States, Portugal and France. Ricardo Vilalta's co-authors include Youssef Drissi, Christophe Giraud-Carrier, Pavel Brazdil, Carlos Soares, Sheng Ma, S. Ma, T. F. Stepinski, Irina Rish, Manish Gupta and R.K. Sahoo and has published in prestigious journals such as Monthly Notices of the Royal Astronomical Society, IEEE Transactions on Geoscience and Remote Sensing and Machine Learning.

In The Last Decade

Ricardo Vilalta

55 papers receiving 1.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

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 Perspective View and Survey of Meta-Learning

2002 667 citations Ricardo Vilalta, Youssef Drissi profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ricardo Vilalta United States	17	1.1k	417	393	304	160	56	2.1k
Ashok N. Srivastava United States	22	1.1k 1.0×	249 0.6×	239 0.6×	186 0.6×	413 2.6×	72	2.2k
Steve Chien United States	34	1.8k 1.6×	1.3k 3.1×	168 0.4×	533 1.8×	106 0.7×	318	4.1k
Mark Johnston United States	26	1.4k 1.3×	846 2.0×	108 0.3×	266 0.9×	196 1.2×	151	3.0k
Kiri L. Wagstaff United States	25	1.7k 1.5×	237 0.6×	353 0.9×	1.0k 3.3×	542 3.4×	121	3.6k
Jianghui Cai China	17	690 0.6×	283 0.7×	234 0.6×	220 0.7×	177 1.1×	73	1.4k
Jeremy Kepner United States	22	308 0.3×	808 1.9×	429 1.1×	354 1.2×	127 0.8×	82	1.9k
Nikunj C. Oza United States	21	1.4k 1.3×	296 0.7×	124 0.3×	355 1.2×	347 2.2×	70	2.2k
Pieter-Tjerk de Boer Netherlands	10	630 0.6×	249 0.6×	111 0.3×	623 2.0×	121 0.8×	49	2.4k
P. A. Estévez Chile	28	1.4k 1.2×	171 0.4×	204 0.5×	923 3.0×	394 2.5×	131	3.5k
Carlos J. Alonso Spain	12	911 0.8×	90 0.2×	178 0.5×	369 1.2×	300 1.9×	47	1.9k

Countries citing papers authored by Ricardo Vilalta

Since Specialization

Citations

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

Fields of papers citing papers by Ricardo Vilalta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ricardo Vilalta

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Schram, Malachi, et al.. (2024). Robust errant beam prognostics with conditional modeling for particle accelerators. Machine Learning Science and Technology. 5(1). 15044–15044. 7 indexed citations

Mroczek, Débora, M. Hjorth‐Jensen, Jacquelyn Noronha-Hostler, et al.. (2023). Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning. Physical review. C. 107(5). 10 indexed citations

Dai, Zhenyu, et al.. (2023). Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. Monthly Notices of the Royal Astronomical Society. 527(2). 3381–3394. 3 indexed citations

Lawler, Andrew, J. Zuntz, Alex I. Malz, et al.. (2020). Ridges in the Dark Energy Survey for cosmic trough identification. Monthly Notices of the Royal Astronomical Society. 500(1). 859–870. 4 indexed citations

Ishida, Émille E. O., S. González–Gaitán, Rafael S. de Souza, et al.. (2020). Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. Institutional Repository of the University of Granada (University of Granada). 3115–3124. 4 indexed citations

Souza, Rafael S. de, Émille E. O. Ishida, Alex I. Malz, et al.. (2019). Stress testing the dark energy equation of state imprint on supernova data. Physical review. D. 99(12). 8 indexed citations

Ishida, Émille E. O., Michele Sasdelli, Ricardo Vilalta, et al.. (2016). Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. Proceedings of the International Astronomical Union. 12(S325). 247–252. 2 indexed citations

Vilalta, Ricardo, et al.. (2016). Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining. Artificial Intelligence in Medicine. 74. 44–52. 27 indexed citations

Souza, Rafael S. de, Ewan Cameron, Madhura Killedar, et al.. (2014). The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression and Numerical Simulations. arXiv (Cornell University). 2 indexed citations

10.

Vilalta, Ricardo, et al.. (2013). A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood. Astronomy and Computing. 2. 46–53. 7 indexed citations

11.

Brazdil, Pavel, Christophe Giraud-Carrier, Carlos Soares, & Ricardo Vilalta. (2009). Metalearning - Applications to Data Mining. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 280 indexed citations

12.

Brazdil, Pavel, Christophe Giraud-Carrier, Carlos Soares, & Ricardo Vilalta. (2008). Metalearning. 127 indexed citations

13.

Stepinski, T. F., Soumya Ghosh, & Ricardo Vilalta. (2007). Machine Learning for Automatic Mapping of Planetary Surfaces.. Innovative Applications of Artificial Intelligence. 1807–1812. 15 indexed citations

14.

Vilalta, Ricardo. (2006). Identifying and Characterizing Class Clusters to Explain Learning Performance. National Conference on Artificial Intelligence. 19–25. 1 indexed citations

15.

Vilalta, Ricardo, et al.. (2004). Piece-wise model fitting using local data patterns. European Conference on Artificial Intelligence. 559–563. 3 indexed citations

16.

Vilalta, Ricardo & T. F. Stepinski. (2004). Thematic Maps of Martian Topography Generated by a Clustering Algorithm. Lunar and Planetary Science Conference. 1169. 1 indexed citations

17.

Stepinski, T. F., et al.. (2003). Algorithmic Classification of Drainage Networks on Mars and its Relation to Martian Geological Units. LPI. 1653. 1 indexed citations

18.

Vilalta, Ricardo & Youssef Drissi. (2002). A Characterization of Difficult Problems in Classification.. 133–138. 7 indexed citations

19.

Vilalta, Ricardo, et al.. (2000). A Quantification of Distance Bias Between Evaluation Metrics In Classification. International Conference on Machine Learning. 1087–1094. 15 indexed citations

20.

Vilalta, Ricardo & Larry Rendell. (1997). Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. International Conference on Machine Learning. 394–402. 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.

Explore authors with similar magnitude of impact