Oxford Statistics researchers Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh and Arnaud Doucet has received an Outstanding Paper Award at NeurIPS 2022, the key conference in Machine Learning and Artificial Intelligence. Their paper Riemmanian Score-Based Generative Modeling generalizes score-based generative model (SGM) from Euclidean space to Riemannian manifolds by identifying major components that contribute to the success of SGMs. The method is both a novel and technically useful contribution.
This is a fantastic achievement, as only 13 papers out of over 8,000 submissions received such an award. Oxford University
What does a statistician do for the England football team?
From squad selection to modelling how footballs behave at altitude, statistician Matt Penn explains how data is helping shape the modern game, and why coaches will always matter more than the numbers.
New evidence suggests vast hidden magma systems inside Mars
Researchers from the Departments of Earth Science and Statistics have found evidence that Mars once hosted enormous, Earth-like magmatic systems deep below its surface – even though the planet lacks the plate tectonics long considered essential for this kind of geological complexity. The findings open up new possibilities for how rocky planets become habitable.
Finding a needle in the genomic haystack: Targeting rare genes using statistical outliers
In statistical modelling, extreme outliers are often written off as 'noise'. But a new study by researchers from Oxford's Department of Statistics and Big Data Institute published this week in The American Journal of Human Genetics reverses that principle, using these outliers as the basis of a targeting system for locating rare, high-impact genetic mutations.