29 citations · 163 across the 31 of their papers we have counts for
33 papers
Orthogonal AMP for Problems with Multiple Measurement Vectors and/or Multiple Transforms
Yiyao Cheng, Lei Liu, Shansuo Liang +2
Approximate message passing (AMP) algorithms break a (high-dimensional) statistical problem into parts then repeatedly solve each part in turn, akin to alternating projections. A d…
Fast Rate Information-theoretic Bounds on Generalization Errors
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various inform…
First order online optimisation using forward gradients in over-parameterised systems
Behnam Mafakheri, Iman Shames, Jonathan H. Manton
The success of deep learning over the past decade mainly relies on gradient-based optimisation and backpropagation. This paper focuses on analysing the performance of first-order g…
On the Generalization for Transfer Learning: An Information-Theoretic Analysis
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In t…
Fast Rate Generalization Error Bounds: Variations on a Theme
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the rele…
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
Xuetong Wu, Mingming Gong, Jonathan H. Manton +2
Recent advancements in unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), particularly incorporating causality, have led to significant methodological improve…