Finding yourself repeatedly training your ML model because you’ve collected more data? Not sure what to do with the hyperparameters? We investigated for you! Check out the paper arxiv.org/abs/2306.16916 with the wonderful Huibin Shen, François-Xavier Aubet, David Salinas and Aaron Klein

I'll be at #ACL2023 next week with 2 papers: ⭐️Extrinsic Evaluation of Machine Translation Metrics (Main conf - Monday Session 2: MT Oral) ⭐️Meta-Learning a Cross-lingual Manifold for Semantic Parsing (TACL - Wednesday Session 7: Semantics) Reach out if you want to say hello!

Congratulations to CDT student Tom! 🎉🎉🎉

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Curious about how noise influences uncertainty calibration and generalisation of neural networks? We'll present our new work on this topic at the #ICML2023 SCIS workshop this Saturday! Collaboration with Martin Ferianc, Timothy Hospedales, Miguel Rodrigues

Cool work including CDT member Kate!

Interested in what could be the long-term implications of deploying too many AI systems that are not fair? You can learn more in our paper - we've updated it on arXiv with the version that will appear soon in the proceedings of the Stanford Existential Risk Conference!

I'm very excited to be in Seoul to give a talk and present a poster about our CVPR'23 Meta Omnium paper at the Hyundai Vision Conference! Feel free to take a look at the poster and also a few photos from this great city! Meta Omnium website: edi-meta-learning.github.io/meta-omnium

Life update: I have passed my PhD defense (alexandra birch and Ivan Vulić were excellent examiners), and moved to New York City! Many thanks to everyone who made the PhD experience so fun, especially my CDT cohort, collaborators, and supervisor Iain Murray

I'm really happy to share the news that Meta-Calibration has been accepted to TMLR! Meta-Calibration uses meta-learning as a new way to optimise for uncertainty calibration of neural networks. I've had a very positive experience with TMLR and certainly recommend submitting there!

Outstanding Papers ACL 2025 #ACL23: Extrinsic Evaluation of Machine Translation Metrics Nikita Moghe,Tom Sherborne,Mark Steedman,Alexandra Birch Compositional Generalization w/o Trees using Multiset Tagging & Latent Permutations Matthias Lindemann,Alexander Koller,Ivan Titov

Excited for the poster session this afternoon at AutoML_conf! I'll be talking about hyperparameter optimisation for increasing data set sizes and learning between tasks. Work with Huibin Shen, François-Xavier Aubet, David Salinas and Aaron Klein #AutoML23

🚨New TACL paper 🚨 Can we use explicit latent variable alignment for cross-lingual transfer? Minotaur uses optimal transport for explicit alignment across languages. w/ Tom Hosking, Mirella Lapata. To be presented EMNLP 2025 + Meral Yıldız in-person 🇸🇬 doi.org/10.1162/tacl_a…

Do you love cross-lingual transfer? Are you interested in putting latent variables everywhere you can? Desperately searching for applied optimal transport research? Come to my poster 16:00 in East Foyer! #EMNLP2023 virtual2023.emnlp.org/paper_TACL-514… w/ Tom Hosking + Mirella Lapata

Poster sessions are happening today! 11:40-1:20 and 5:00-5:30 today in room 208-210. Come chat transfer learning for Gaussian processes and Bayesian optimisation 🌸

Congratulations Tom! The hard work pays off!