The ‘Information, Inference and Machine Learning’ group focuses on the foundations and applications of information theory, information processing, and machine learning systems
Concretely, our current research concentrates on the information-theoreticfoundations of learning paradigms such as supervised learning, unsupervised learning, deep learning and meta-learning.
It also concentrates — in collaboration with domain experts — on applications of machine learning systems to a variety of areas including arts and humanities and climate science.
Our research is funded by diverse bodies such as EPSRC, InnovateUK, H2020, Royal Society and Industry
Publications » Projects »Our papers ‘Optimization Guarantees for ISTA and ADMM based Unfolded Networks’ and ‘Blind Unmixing Using a Double Deep Image Prior’ have been accepted at IEEE ICASSP 2022.
Our papers ‘A Theoretical-Inspired Semi-supervised Learning Algorithm under Covariate-shift’ and ‘Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm’ have been accepted at AISTATS 2022.
Jaweria Amjad has successfully defended her PhD in December 2021.
Our paper ‘An Exact Characterization of the Generalization Error for the Gibbs Algorithm’ has been accepted at NeurIPS 2021.
Our paper ‘Blind Pareto Fairness’ has been accepted at ICML 2021.
Zhuo Zhi has joined the ‘Information, Inference and Machine Learning’ group in October 2021.
Mathieu Alain has joined the ‘Information, Inference and Machine Learning’ group in October 2021.
Maria Carolina Novitsari has joined the ‘Information, Inference and Machine Learning’ group in October 2020.
Martin Ferianc has joined the ‘Information, Inference and Machine Learning’ group in October 2019.
Zahra Sabetsarvestani has successfully defended her PhD in September 2019.