2 citations · 6 across the 14 of their papers we have counts for
4 papers · 1 filter
Canonical Regularisation of Wide Feature-Learning Neural Networks
George Whittle, Pranav Vaidhyanathan, Juliusz Ziomek +2
Wide neural networks in the feature-learning regime drive modern deep learning, and yet they remain far less studied than their kernel-regime counterparts. We consider a critical y…
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
George Whittle, Juliusz Ziomek, Jacob Rawling +1
While Bayesian inference provides a principled framework for reasoning under uncertainty, its widespread adoption is limited by the intractability of exact posterior computation, n…
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
Juliusz Ziomek, Masaki Adachi, Michael A. Osborne
Bayesian Optimization (BO) is widely used for optimising black-box functions but requires us to specify the length scale hyperparameter, which defines the smoothness of the functio…
Settling the Communication Complexity for Distributed Offline Reinforcement Learning
Juliusz Krysztof Ziomek, Jun Wang, Yaodong Yang
We study a novel setting in offline reinforcement learning (RL) where a number of distributed machines jointly cooperate to solve the problem but only one single round of communica…