Gal Elidan

3.3k total citations
45 papers, 1.6k citations indexed

About

Gal Elidan is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Vision and Pattern Recognition. According to data from OpenAlex, Gal Elidan has authored 45 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Gal Elidan's work include Bayesian Modeling and Causal Inference (20 papers), Bayesian Methods and Mixture Models (11 papers) and Machine Learning and Data Classification (7 papers). Gal Elidan is often cited by papers focused on Bayesian Modeling and Causal Inference (20 papers), Bayesian Methods and Mixture Models (11 papers) and Machine Learning and Data Classification (7 papers). Gal Elidan collaborates with scholars based in Israel, United States and Canada. Gal Elidan's co-authors include Nir Friedman, Daphne Koller, Dana Pe’er, Aviv Regev, Stephen Jay Gould, David S. Cohen, Tommy Kaplan, Yoseph Barash, Ian McGraw and Geremy Heitz and has published in prestigious journals such as Bioinformatics, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Gal Elidan

42 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gal Elidan Israel	18	607	575	389	90	77	45	1.6k
Luis Gerardo de la Fraga Mexico	20	245 0.4×	271 0.5×	389 1.0×	202 2.2×	39 0.5×	80	1.3k
Guillaume Obozinski Switzerland	15	258 0.4×	700 1.2×	588 1.5×	78 0.9×	95 1.2×	38	2.0k
Andrew Delong Canada	11	1.7k 2.7×	470 0.8×	692 1.8×	166 1.8×	175 2.3×	14	3.2k
E.R. Dougherty United States	24	1.5k 2.4×	382 0.7×	241 0.6×	282 3.1×	229 3.0×	65	2.2k
Reinhard Heckel United States	19	1.0k 1.7×	331 0.6×	197 0.5×	233 2.6×	7 0.1×	56	1.6k
John Goutsias United States	23	530 0.9×	228 0.4×	873 2.2×	132 1.5×	112 1.5×	87	1.9k
Sören Sonnenburg Germany	17	818 1.3×	900 1.6×	831 2.1×	160 1.8×	78 1.0×	20	2.4k
Raj Acharya United States	19	423 0.7×	145 0.3×	412 1.1×	38 0.4×	58 0.8×	132	1.4k
I. García Spain	20	135 0.2×	267 0.5×	188 0.5×	263 2.9×	17 0.2×	121	1.6k

Countries citing papers authored by Gal Elidan

Since Specialization

Citations

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

Fields of papers citing papers by Gal Elidan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gal Elidan

This figure shows the co-authorship network connecting the top 25 collaborators of Gal Elidan. A scholar is included among the top collaborators of Gal Elidan 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 Gal Elidan. Gal Elidan 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

Lang, Oran, Gal Elidan, Avinatan Hassidim, et al.. (2021). Explaining in Style: Training a GAN to explain a classifier in StyleSpace. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 673–682. 53 indexed citations

Elidan, Gal, et al.. (2019). Unmixing $K$ -Gaussians With Application to Hyperspectral Imaging. IEEE Transactions on Geoscience and Remote Sensing. 57(9). 7281–7293. 9 indexed citations

Elidan, Gal, et al.. (2019). Globally Optimal Learning for Structured Elliptical Losses. Neural Information Processing Systems. 32. 13488–13497. 2 indexed citations

Livni, Roi, et al.. (2016). Improper Deep Kernels. International Conference on Artificial Intelligence and Statistics. 1159–1167.

Eban, Elad, et al.. (2016). Scalable Learning of Non-Decomposable Objectives. International Conference on Artificial Intelligence and Statistics. 832–840. 15 indexed citations

Eban, Elad, Gideon Rothschild, Adi Mizrahi, Israel Nelken, & Gal Elidan. (2013). Dynamic Copula Networks for Modeling Real-valued Time Series. International Conference on Artificial Intelligence and Statistics. 247–255. 8 indexed citations

Elidan, Gal, et al.. (2012). Nonparanormal Belief Propagation (NPNBP). Neural Information Processing Systems. 25. 899–907. 1 indexed citations

Elidan, Gal. (2012). Copula Network Classifiers (CNCs). International Conference on Artificial Intelligence and Statistics. 346–354. 2 indexed citations

Elidan, Gal. (2012). Lightning-speed Structure Learning of Nonlinear Continuous Networks. International Conference on Artificial Intelligence and Statistics. 355–363. 3 indexed citations

10.

Elidan, Gal. (2011). Bagged Structure Learning of Bayesian Network. International Conference on Artificial Intelligence and Statistics. 251–259. 3 indexed citations

11.

Elidan, Gal. (2010). Copula Bayesian Networks. Neural Information Processing Systems. 23. 559–567. 64 indexed citations

12.

Jaimovich, Ariel, Ofer Meshi, Ian McGraw, & Gal Elidan. (2010). FastInf: An Efficient Approximate Inference Library. Journal of Machine Learning Research. 11(57). 1733–1736. 8 indexed citations

13.

Elidan, Gal. (2010). Inference-less density estimation using copula Bayesian networks. Uncertainty in Artificial Intelligence. 151–159. 4 indexed citations

14.

Elidan, Gal, Iftach Nachman, & Nir Friedman. (2007). Ideal Parent Structure Learning for Continuous Variable Bayesian Networks. Journal of Machine Learning Research. 8(63). 1799–1833. 22 indexed citations

15.

Elidan, Gal & Nir Friedman. (2005). Learning Hidden Variable Networks: The Information Bottleneck Approach. Journal of Machine Learning Research. 6(4). 81–127. 44 indexed citations

16.

Nachman, Iftach, Gal Elidan, & Nir Friedman. (2004). "Ideal Parent" structure learning for continuous variable networks. arXiv (Cornell University). 400–409. 10 indexed citations

17.

Barash, Yoseph, Gal Elidan, Tommy Kaplan, & Nir Friedman. (2004). CIS: compound importance sampling method for protein–DNA binding site p-value estimation. Bioinformatics. 21(5). 596–600. 23 indexed citations

18.

Elidan, Gal, et al.. (2002). Data perturbation for escaping local maxima in learning. National Conference on Artificial Intelligence. 132–139. 47 indexed citations

19.

Pe’er, Dana, Aviv Regev, Gal Elidan, & Nir Friedman. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics. 17(suppl_1). S215–S224. 347 indexed citations

20.

Elidan, Gal, et al.. (2000). Discovering Hidden Variables: A Structure-Based Approach. Neural Information Processing Systems. 13. 479–485. 58 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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