398 citations · 1.8k across the 163 of their papers we have counts for
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Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers
Tomer Galanti, András György, Marcus Hutter
We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that repre…
Testing Independence of Exchangeable Random Variables
Marcus Hutter
Given well-shuffled data, can we determine whether the data items are statistically (in)dependent? Formally, we consider the problem of testing whether a set of exchangeable random…
Sequential Learning Of Neural Networks for Prequential MDL
Jorg Bornschein, Yazhe Li, Marcus Hutter
Minimum Description Length (MDL) provides a framework and an objective for principled model evaluation. It formalizes Occam's Razor and can be applied to data from non-stationary s…
Beyond Bayes-optimality: meta-learning what you know you don't know
Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch +11
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rath…
Atari-5: Distilling the Arcade Learning Environment down to Five Games
Matthew Aitchison, Penny Sweetser, Marcus Hutter
The Arcade Learning Environment (ALE) has become an essential benchmark for assessing the performance of reinforcement learning algorithms. However, the computational cost of gener…
Formal Algorithms for Transformers
Mary Phuong, Marcus Hutter
This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they ar…