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Joaquin Vanschoren

Joaquin Vanschoren has 2 papers in our library, in 2026, most of them on Language models & pretraining, Efficient models & compression; often with Branislav Pecher, Ivan Srba, Maria Bielikova.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 38 · 224 works · 7,097 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 226 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9460

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Eindhoven University of TechnologyEindhoven · Netherlands2014–2026 · 128 papersEmploymentcurrent🤝Kempelen Institute of Intelligent TechnologiesBratislava · Slovakia2026 · 1 papercurrent🎓KU LeuvenLeuven · Belgium2006–2013 · 18 papers🎓Leiden UniversityLeiden · Netherlands1996–2014 · 11 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026 · 15 citations)
    Picking which labelled examples to use strongly affects few-shot performance. Methods for large language models tend to rely on selectors built for specific in-context learning setups and overlook classic strategies from supervised learning, such as active learning and core-set methods. The authors propose ACSESS…
  2. An Empirical Study of Speculative Decoding for Small Language Models (2026)
    Speculative decoding speeds up LLM inference: a cheap drafter proposes tokens, and the target model checks them in a single forward pass.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Automatic Combination of Sample Selection Strategies for Few-Shot LearningFindings of the Association for Computational LinguisticsBranislav Pecher, Ivan Srba, Maria Bielikova, et al.2026 · 15 citations
  2. An Empirical Study of Speculative Decoding for Small Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Luca Mainardi, Selcuk Sandikci, Joaquin Vanschoren2026
  3. TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning ↗arXiv:2606.01498 · no summary yetYaxuan Kong et al.2026

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 226 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

Updated

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