Liva Ralaivola

2.1k total citations
26 papers, 1.0k citations indexed

About

Liva Ralaivola is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Liva Ralaivola has authored 26 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 6 papers in Computational Mechanics and 6 papers in Computational Theory and Mathematics. Recurrent topics in Liva Ralaivola's work include Machine Learning and Algorithms (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Computational Drug Discovery Methods (4 papers). Liva Ralaivola is often cited by papers focused on Machine Learning and Algorithms (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Computational Drug Discovery Methods (4 papers). Liva Ralaivola collaborates with scholars based in France, United States and Canada. Liva Ralaivola's co-authors include Pierre Baldi, S. Joshua Swamidass, Florence d’Alché–Buc, Hiroto Saigo, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Jonathan H. Chen, Jocelyne Bruand and Pierre Mahé and has published in prestigious journals such as Bioinformatics, PLoS ONE and IEEE Transactions on Signal Processing.

In The Last Decade

Liva Ralaivola

23 papers receiving 978 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Liva Ralaivola France	11	550	348	299	130	92	26	1.0k
Robin Winter Germany	9	316 0.6×	496 1.4×	575 1.9×	358 2.8×	193 2.1×	10	1.3k
Wentao Zhao China	12	338 0.6×	146 0.4×	374 1.3×	39 0.3×	164 1.8×	63	1.2k
Twan van Laarhoven Netherlands	11	860 1.6×	528 1.5×	148 0.5×	109 0.8×	66 0.7×	28	1.5k
Shen Wang China	21	211 0.4×	529 1.5×	909 3.0×	53 0.4×	681 7.4×	87	1.7k
Zhe Quan China	14	265 0.5×	319 0.9×	263 0.9×	115 0.9×	75 0.8×	44	780
Sendong Zhao China	10	294 0.5×	191 0.5×	426 1.4×	101 0.8×	102 1.1×	30	759
Jonathan Greene United States	16	321 0.6×	498 1.4×	350 1.2×	30 0.2×	126 1.4×	30	1.4k
Andy Davis United States	6	158 0.3×	87 0.3×	197 0.7×	52 0.4×	102 1.1×	8	730
Minghu Song United States	10	271 0.5×	162 0.5×	140 0.5×	33 0.3×	142 1.5×	14	678

Countries citing papers authored by Liva Ralaivola

Since Specialization

Citations

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

Fields of papers citing papers by Liva Ralaivola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liva Ralaivola

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

All Works

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20 of 20 papers shown

Ralaivola, Liva, et al.. (2022). Scalable Ridge Leverage Score Sampling for the Nyström Method. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4163–4167.

Emiya, Valentin, et al.. (2021). QuicK-means: accelerating inference for K-means by learning fast transforms. Machine Learning. 110(5). 881–905. 5 indexed citations

Molfetta, Giuseppe Di, et al.. (2020). Quantum bandits. Quantum Machine Intelligence. 2(1). 10 indexed citations

Laviolette, François, et al.. (2016). Risk upper bounds for general ensemble methods with an application to multiclass classification. Neurocomputing. 219. 15–25. 1 indexed citations

Rakotomamonjy, Alain, et al.. (2016). Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization. IEEE Transactions on Neural Networks and Learning Systems. 28(11). 2789–2802. 8 indexed citations

Audiffren, Julien & Liva Ralaivola. (2015). Cornering stationary and restless mixing bandits with Remix-UCB. Neural Information Processing Systems. 28. 3339–3347. 1 indexed citations

Zhong, Hongliang, et al.. (2015). Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach. The European Symposium on Artificial Neural Networks. 1 indexed citations

Ralaivola, Liva, et al.. (2015). From cutting planes algorithms to compression schemes and active learning. arXiv (Cornell University). 1–8. 1 indexed citations

Proix, Timothée, et al.. (2015). Reward-based online learning in non-stationary environments: Adapting a P300-speller with a “backspace” key. 100. 1–8. 1 indexed citations

10.

Takerkart, Sylvain & Liva Ralaivola. (2014). Multiple Subject Learning for Inter-Subject Prediction. HAL (Le Centre pour la Communication Scientifique Directe).

11.

Takerkart, Sylvain, Guillaume Auzias, Bertrand Thirion, & Liva Ralaivola. (2014). Graph-Based Inter-Subject Pattern Analysis of fMRI Data. PLoS ONE. 9(8). e104586–e104586. 19 indexed citations

12.

Ralaivola, Liva. (2009). Semi-supervised Bipartite Ranking with the Normalized Rayleigh Coefficient. The European Symposium on Artificial Neural Networks. 2 indexed citations

13.

Azencott, Chloé‐Agathe, et al.. (2007). One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. Journal of Chemical Information and Modeling. 47(3). 965–974. 42 indexed citations

14.

Denis, François, Chr̀istophe Magnan, & Liva Ralaivola. (2006). Efficient learning of Naive Bayes classifiers under class-conditional classification noise. 265–272. 6 indexed citations

15.

Ralaivola, Liva & Florence d’Alché–Buc. (2006). Time series filtering, smoothing and learning using the kernel kalman filter. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.. 3. 1449–1454. 34 indexed citations

16.

Ralaivola, Liva, et al.. (2005). SVM and Pattern-Enriched Common Fate Graphs for the Game of Go. The European Symposium on Artificial Neural Networks. 485–490. 16 indexed citations

17.

Ralaivola, Liva, S. Joshua Swamidass, Hiroto Saigo, & Pierre Baldi. (2005). Graph kernels for chemical informatics. Neural Networks. 18(8). 1093–1110. 286 indexed citations

18.

Swamidass, S. Joshua, et al.. (2005). Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Computer applications in the biosciences. 21(Suppl 1). i359–i368. 124 indexed citations

19.

Ralaivola, Liva & Florence d’Alché–Buc. (2003). Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. Neural Information Processing Systems. 16. 129–136. 39 indexed citations

20.

Ralaivola, Liva, et al.. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics. 19(suppl_2). ii138–ii148. 294 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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