Incorporating network based protein complex discovery into automated model construction. - Paul Scherer; Maja Trebacz; Nikola Simidjievski; Zohreh Shams; Helena Andrés-Terré; Pietro Liò; Mateja Jamnik (CoRR, 2020)

Using ontology embeddings for structural inductive bias in gene expression data analysis. - Maja Trebacz; Zohreh Shams; Mateja Jamnik; Paul Scherer; Nikola Simidjievski; Helena Andrés-Terré; Pietro Liò (CoRR, 2020)

Open-Ended Learning Leads to Generally Capable Agents. - Open Ended Learning Team; Adam Stooke; Anuj Mahajan; Catarina Barros; Charlie Deck; Jakob Bauer; Jakub Sygnowski; Maja Trebacz; Max Jaderberg; Michaël Mathieu; Nat McAleese; Nathalie Bradley-Schmieg; et al. (CoRR, 2021)

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. - Paul Scherer; Maja Trebacz; Nikola Simidjievski; Ramón Viñas 001; Zohreh Shams; Helena Andrés-Terré; Mateja Jamnik; Pietro Liò (Bioinform., 2022)

Teaching language models to support answers with verified quotes. - Jacob Menick; Maja Trebacz; Vladimir Mikulik; John Aslanides; H. Francis Song; Martin J. Chadwick; Mia Glaese; Susannah Young; Lucy Campbell-Gillingham; Geoffrey Irving; Nat McAleese (CoRR, 2022)

Improving alignment of dialogue agents via targeted human judgements. - Amelia Glaese; Nat McAleese; Maja Trebacz; John Aslanides; Vlad Firoiu; Timo Ewalds; Maribeth Rauh; Laura Weidinger; Martin J. Chadwick; Phoebe Thacker; Lucy Campbell-Gillingham; Jonathan Uesato; et al. (CoRR, 2022)

LLM Critics Help Catch LLM Bugs. - Nat McAleese; Rai Michael Pokorny; Juan Felipe Ceron Uribe; Evgenia Nitishinskaya; Maja Trebacz; Jan Leike (CoRR, 2024)