The Department of Statistics at Oxford is a world leader in research including computational statistics and statistical methodology, applied probability, bioinformatics and mathematical genetics. In the 2014 Research Excellence Framework (REF), Oxford's Mathematical Sciences submission was ranked overall best in the UK.
This is an exciting time for the Department. We have now moved into our new home on St Giles and we are currently settling in.
The new building provides improved lecture and teaching space, a variety of interaction areas, and brings together researchers in Probability and Statistics. It has created a highly visible centre for the Department in Oxford.
Since 2010, the Department has been awarded over forty research grants with a total value of £9M, not counting several very large EPSRC and MRC funded awards for Centres for doctoral training.The main sponsors are the European Commission, EPSRC, the Medical Research Council and the Wellcome Trust.
We offer an undergraduate degree (BA or MMath) in Mathematics and Statistics, jointly with the Mathematical Institute.
At postgraduate level there is an MSc course in Applied Statistics, as well as a lively and stimulating environment for postgraduate research (DPhil or MSc by Research). Our graduates are employed in a wide range of occupational sectors throughout the world, including the university sector.
The Department co-hosts the EPSRC and MRC Centre for Doctoral Training (CDT) in Next-Generational Statistical Science- the Oxford-Warwick Statistics Programme OxWaSP.
A Theory of Weak-Supervision and Zero-Shot Learning
A lecture exploring alternatives to using labeled training data. Labeled training data is often scarce, unavailable, or can be very costly to obtain. To circumvent this problem, there is a growing interest in developing methods that can exploit sources of information other than labeled data, such as weak-supervision and zero-shot learning. While these techniques obtained impressive accuracy in practice, both for vision and language domains, they come with no theoretical characterization of their accuracy. In a sequence of recent works, we develop a rigorous mathematical framework for constructing and analyzing algorithms that combine multiple sources of related data to solve a new learning task. Our learning algorithms provably converge to models that have minimum empirical risk with respect to an adversarial choice over feasible labelings for a set of unlabeled data, where the feasibility of a labeling is computed through constraints defined by estimated statistics of the sources. Notably, these methods do not require the related sources to have the same labeling space as the multiclass classification task. We demonstrate the effectiveness of our approach with experimentations on various image classification tasks. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/
6 Apr 2022
Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction
A high-level overview of key areas of AI ethics and not-ethics, exploring the challenges of algorithmic decision-making, kinds of bias, and interpretability, linking these issues to problems of human-system interaction. Much attention is now being focused on AI Ethics and Safety, with the EU AI Act and other emerging legislation being proposed to identify and curb "AI risks" worldwide. Are such ethical concerns unique to AI systems - and not just digital systems in general?
Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/
5 Apr 2022
The practicalities of academic research ethics - how to get things done
A brief introduction to various legal and procedural ethical concepts and their applications within and beyond academia. It's all very well to talk about truth, beauty and justice for academic research ethics. But how do you do these things at a practical level? If you have a big idea, or stumble across something with important implications, what do you do with it? How do you make sure you've got appropriate safeguards without drowning in bureaucracy? Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/
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