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Showing papers from Gatsby Computational Neuroscience Unit, UCL Show all papers

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NeurIPS 2026Gatsby Computational NeurosciencUniversity College London, UniveIST AustriaStanfordDeep learning theory

Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

High-dimensional analysis of pretraining via PCA and linear probing derives exact errors versus representation size, showing compression helps with abundant unlabeled but scarce labeled data.

Valentina Njaradi, Clémentine Dominé, Rachel A Swanson, Marco Mondelli and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026University College London, UniveGatsby Computational NeurosciencGoogle Deepmind / UCLKernels & Gaussian processes

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity

A Hilbert-valued one-step estimator enables semiparametrically efficient inference and bootstrap-calibrated tests for kernel noise heterogeneity in additive noise models.

Jakub Wornbard, Zikai Shen, Dimitri Meunier, Arthur Gretton

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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