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Daniele Calandriello

Daniele Calandriello has 2 papers in our library, in 2023; often with Mark Rowland, Michal Valko, Mohammad Gheshlaghi Azar.

2 papers in our library · 2023

OpenAlex · h-index 12 · 63 works · 408 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 280 times in our library · ⭐ Paper Stars ranking: #5770

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

🏢Google DeepMindLondon · United Kingdom2020–2026 · 10 paperscurrent🔬Italian Institute of TechnologyGenoa · Italy2018–2026 · 6 paperscurrent🔬Centre Inria de l'Université de LilleVilleneuve-d'Ascq · France2015–2026 · 4 paperscurrent🔬Institut national de recherche en sciences et technologies du numériqueLe Chesnay-Rocquencourt · France2015–2026 · 4 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2018–2022 · 5 papers🎓Politecnico di MilanoMilan · Italy2013–2014 · 4 papers🏢GoogleMountain View · United States2020–2024 · 3 papers🔬IIT@MITCambridge · United States2018–2020 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A General Theoretical Paradigm to Understand Learning from Human Preferences (2023 · 263 citations)
    This paper by researchers at Google DeepMind provides a theoretical foundation for methods that train language models from human preferences.
  2. Nash Learning from Human Feedback (2023 · 32 citations)
    Standard RLHF reduces pairwise human judgments to a scalar reward, typically through a Bradley-Terry model, and then optimizes the LLM against that score.

 Co-authors here

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

 Cited by

Papers of our library cite their works 280 times (of the 149,439 papers whose bibliographies we read); 75 citing papers with a page here →.

Updated

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