Ürün Doǧan

582 total citations
14 papers, 190 citations indexed

About

Ürün Doǧan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Ürün Doǧan has authored 14 papers receiving a total of 190 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Ürün Doǧan's work include Face and Expression Recognition (5 papers), Machine Learning and Algorithms (4 papers) and Machine Learning and ELM (3 papers). Ürün Doǧan is often cited by papers focused on Face and Expression Recognition (5 papers), Machine Learning and Algorithms (4 papers) and Machine Learning and ELM (3 papers). Ürün Doǧan collaborates with scholars based in United States, Germany and United Kingdom. Ürün Doǧan's co-authors include Johann Edelbrunner, Ioannis Iossifidis, Tobias Glasmachers, Marius Kloft, Yunwen Lei, Christian Igel, Ding‐Xuan Zhou, Alexander Binder, Gilles Blanchard and Maximilian Alber and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Information Theory and Machine Learning.

In The Last Decade

Ürün Doǧan

13 papers receiving 184 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ürün Doǧan United States	8	120	68	36	29	17	14	190
Shane Barratt United States	5	54 0.5×	21 0.3×	32 0.9×	19 0.7×	5 0.3×	8	150
Cijo Jose Switzerland	4	77 0.6×	130 1.9×	23 0.6×	14 0.5×	5 0.3×	5	188
Ibrahim Sobh Egypt	6	54 0.5×	77 1.1×	59 1.6×	32 1.1×	8 0.5×	15	159
Thomas Kipf Netherlands	7	120 1.0×	87 1.3×	15 0.4×	7 0.2×	32 1.9×	10	184
Jiachen Sun United States	6	49 0.4×	46 0.7×	30 0.8×	12 0.4×	18 1.1×	12	177
Shih-Yang Su Taiwan	6	44 0.4×	219 3.2×	15 0.4×	16 0.6×	9 0.5×	8	266
Inas Jawad Kadhim Iraq	6	43 0.4×	367 5.4×	9 0.3×	14 0.5×	29 1.7×	15	422
Matias Korman Japan	8	28 0.2×	85 1.3×	8 0.2×	3 0.1×	40 2.4×	68	214
Andrzej Przybył Poland	7	65 0.5×	18 0.3×	4 0.1×	53 1.8×	7 0.4×	10	130
Maria Luisa Merani Italy	12	30 0.3×	31 0.5×	29 0.8×	23 0.8×	18 1.1×	72	407

Countries citing papers authored by Ürün Doǧan

Since Specialization

Citations

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

Fields of papers citing papers by Ürün Doǧan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ürün Doǧan

This figure shows the co-authorship network connecting the top 25 collaborators of Ürün Doǧan. A scholar is included among the top collaborators of Ürün Doǧan 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 Ürün Doǧan. Ürün Doǧan 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:

14 of 14 papers shown

Chitnis, Rohan, et al.. (2024). IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control. 9154–9160.

Manavoglu, Eren, et al.. (2022). Representation learning for clustering via building consensus. Machine Learning. 111(12). 4601–4638. 2 indexed citations

Lei, Yunwen, Ürün Doǧan, Ding‐Xuan Zhou, & Marius Kloft. (2019). Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Transactions on Information Theory. 65(5). 2995–3021. 27 indexed citations

Lei, Yunwen, Ürün Doǧan, Ding‐Xuan Zhou, & Marius Kloft. (2017). Generalization Error Bounds for Extreme Multi-class Classification.. arXiv (Cornell University). 1 indexed citations

Doǧan, Ürün, et al.. (2017). Multi-Task Learning for Contextual Bandits. Neural Information Processing Systems. 30. 4848–4856. 6 indexed citations

Alber, Maximilian, et al.. (2017). Distributed optimization of multi-class SVMs. PLoS ONE. 12(6). e0178161–e0178161. 8 indexed citations

Doǧan, Ürün, et al.. (2016). Decoding multitask DQN in the world of Minecraft. 3 indexed citations

Doǧan, Ürün, Tobias Glasmachers, & Christian Igel. (2016). A unified view on multi-class support vector classification. Journal of Machine Learning Research. 17(1). 1550–1831. 40 indexed citations

Chou, Philip A., et al.. (2016). Prediction of Bandwidth and Additive Metrics for Large Scale Network Tomography. 2 indexed citations

10.

Lei, Yunwen, Alexander Binder, Ürün Doǧan, & Marius Kloft. (2015). Theory and Algorithms for the Localized Setting of Learning Kernels. Neural Information Processing Systems. 173–195. 8 indexed citations

11.

Doǧan, Ürün, et al.. (2015). Extensions of stability selection using subsamples of observations and covariates. Statistics and Computing. 26(5). 1059–1077. 12 indexed citations

12.

Lei, Yunwen, Ürün Doǧan, Alexander Binder, & Marius Kloft. (2015). Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. arXiv (Cornell University). 28. 2035–2043. 14 indexed citations

13.

Glasmachers, Tobias & Ürün Doǧan. (2013). Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. Asian Conference on Machine Learning. 72–86. 10 indexed citations

14.

Doǧan, Ürün, Johann Edelbrunner, & Ioannis Iossifidis. (2011). Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior. 1837–1843. 57 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