Yasemin Altün

6.4k total citations · 3 hit papers
41 papers, 3.8k citations indexed

About

Yasemin Altün is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Yasemin Altün has authored 41 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Theory and Mathematics. Recurrent topics in Yasemin Altün's work include Natural Language Processing Techniques (17 papers), Topic Modeling (14 papers) and Machine Learning and Algorithms (6 papers). Yasemin Altün is often cited by papers focused on Natural Language Processing Techniques (17 papers), Topic Modeling (14 papers) and Machine Learning and Algorithms (6 papers). Yasemin Altün collaborates with scholars based in United States, Germany and United Kingdom. Yasemin Altün's co-authors include Thomas Hofmann, Ioannis Tsochantaridis, Thorsten Joachims, Jan Peters, Katharina Mülling, Moritz Grosse‐Wentrup, Morteza Alamgir, Bernhard Schölkopf, Massimiliano Ciaramita and Vinay Jayaram and has published in prestigious journals such as BMC Bioinformatics, Journal of Machine Learning Research and Lecture notes in computer science.

In The Last Decade

Yasemin Altün

37 papers receiving 3.5k citations

Hit Papers

align trajectories

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.

Large Margin Methods for Structured and Interdependent Output Variables

2005 1.2k citations Ioannis Tsochantaridis, Thorsten Joachims et al. Journal of Machine Learning Research profile →
Support vector machine learning for interdependent and structured output spaces

2004 826 citations Ioannis Tsochantaridis, Thomas Hofmann et al. profile →
Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail

2016 314 citations Morteza Alamgir, Yasemin Altün et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yasemin Altün United States	21	2.4k	1.3k	390	354	314	41	3.8k
David R. Hardoon United Kingdom	18	1.3k 0.5×	1.8k 1.4×	357 0.9×	452 1.3×	248 0.8×	42	3.4k
Dong Huang China	38	2.2k 0.9×	2.7k 2.1×	185 0.5×	275 0.8×	149 0.5×	175	4.6k
Jérôme Louradour France	11	2.1k 0.9×	1.6k 1.3×	115 0.3×	337 1.0×	99 0.3×	27	3.7k
Neil C. Rabinowitz United Kingdom	10	3.0k 1.2×	1.6k 1.2×	733 1.9×	196 0.6×	80 0.3×	14	4.6k
Andrew B. Goldberg United States	17	1.8k 0.7×	871 0.7×	96 0.2×	241 0.7×	198 0.6×	35	3.0k
Bernhard Nebel Germany	34	3.6k 1.5×	982 0.8×	102 0.3×	403 1.1×	147 0.5×	165	5.0k
Sridhar Mahadevan United States	30	2.4k 1.0×	697 0.5×	294 0.8×	112 0.3×	140 0.4×	115	3.9k
Thomas Dean United States	30	3.1k 1.3×	673 0.5×	196 0.5×	408 1.2×	111 0.4×	85	4.6k
Amir Globerson Israel	26	2.0k 0.8×	957 0.7×	146 0.4×	266 0.8×	181 0.6×	94	2.8k

Countries citing papers authored by Yasemin Altün

Since Specialization

Citations

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

Fields of papers citing papers by Yasemin Altün

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasemin Altün

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

Bodur, Gönül, et al.. (2025). Evaluating the effectiveness of virtual reality simulation in cpr training for nursing students: A randomized controlled trial. Nurse Education in Practice. 87. 104486–104486.

Piccinno, Francesco, et al.. (2024). Multimodal Chart Retrieval: A Comparison of Text, Table and Image Based Approaches. 5488–5505. 1 indexed citations

Liu, Fangyu, Francesco Piccinno, Chenxi Pang, et al.. (2023). MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering. 12756–12770. 20 indexed citations

Mehri, Shikib, Yasemin Altün, & Maxine Eskénazi. (2022). LAD: Language Models as Data for Zero-Shot Dialog. 595–604. 3 indexed citations

Andrejczuk, Ewa, et al.. (2022). Table-To-Text generation and pre-training with TabT5. 6758–6766. 10 indexed citations

Qu, Zhongdi, et al.. (2021). Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data. arXiv (Cornell University). 3272–3284. 7 indexed citations

Peters, Jan, Katharina Mülling, & Yasemin Altün. (2015). Seminar in Artificial Intelligence.

Filippova, Katja & Yasemin Altün. (2013). Overcoming the Lack of Parallel Data in Sentence Compression. 1481–1491. 75 indexed citations

Alamgir, Morteza, Moritz Grosse‐Wentrup, & Yasemin Altün. (2010). Multitask Learning for Brain-Computer Interfaces. Max Planck Institute for Plasma Physics. 17–24. 62 indexed citations

10.

Erkan, Alper Nabi, Gustau Camps‐Valls, & Yasemin Altün. (2010). Semi-supervised remote sensing image classification via maximum entropy. 9. 313–318. 9 indexed citations

11.

Widmer, Christian, Nora C. Toussaint, Yasemin Altün, & Gunnar Rätsch. (2010). Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. BMC Bioinformatics. 11(S8). S5–S5. 27 indexed citations

12.

Shi, Qinfeng, Yasemin Altün, Alex Smola, & S. V. N. Vishwanathan. (2007). Semi-Markov Models for Sequence Segmentation. Empirical Methods in Natural Language Processing. 81. 640–648. 8 indexed citations

13.

Altün, Yasemin, David McAllester, & Mikhail Belkin. (2005). Margin Semi-Supervised Learning for Structured Variables.. Neural Information Processing Systems. 33–40. 6 indexed citations

14.

Tsochantaridis, Ioannis, Thorsten Joachims, Thomas Hofmann, & Yasemin Altün. (2005). Large Margin Methods for Structured and Interdependent Output Variables. Journal of Machine Learning Research. 6(50). 1453–1484. 1221 indexed citations breakdown →

15.

Altün, Yasemin, David McAllester, & Mikhail Belkin. (2005). Maximum Margin Semi-Supervised Learning for Structured Variables. MPG.PuRe (Max Planck Society). 18. 33–40. 70 indexed citations

16.

Altün, Yasemin, Alex Smola, & Thomas Hofmann. (2004). Exponential families for conditional random fields. Uncertainty in Artificial Intelligence. 2–9. 21 indexed citations

17.

Altün, Yasemin, et al.. (2003). Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences. MPG.PuRe (Max Planck Society). 145–152. 8 indexed citations

18.

Altün, Yasemin, Ioannis Tsochantaridis, & Thomas Hofmann. (2003). Hidden Markov support vector machines. MPG.PuRe (Max Planck Society). 3–10. 304 indexed citations

19.

Altün, Yasemin, Mark Johnson, & Thomas Hofmann. (2003). Investigating loss functions and optimization methods for discriminative learning of label sequences. 10. 145–152. 25 indexed citations

20.

Altün, Yasemin, Thomas Hofmann, & Mark Johnson. (2002). Discriminative Learning for Label Sequences via Boosting. MPG.PuRe (Max Planck Society). 15. 1001–1008. 32 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