A Theory of Weak-Supervision and Zero-Shot Learning
Department of Statistics | University of Oxford
by Eli Upfal
2y ago
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 construct ..read more
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Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction
Department of Statistics | University of Oxford
by Max Van Kleek
2y ago
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 ..read more
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The practicalities of academic research ethics - how to get things done
Department of Statistics | University of Oxford
by Katherine Fletcher
2y ago
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 ..read more
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Statistics, ethical and unethical: Some historical vignettes
Department of Statistics | University of Oxford
by David Steinsaltz
2y ago
David Steinsaltz gives a lecture on the ethical issues in statistics using historical examples ..read more
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Joining Bayesian submodels with Markov melding
Department of Statistics | University of Oxford
by Robert Goudie
2y ago
This seminar explains and illustrates the approach of Markov melding for joint analysis. Integrating multiple sources of data into a joint analysis provides more precise estimates and reduces the risk of biases introduced by using only partial data. However, it can be difficult to conduct a joint analysis in practice. Instead each data source is typically modelled separately, but this results in uncertainty not being fully propagated. We propose to address this problem using a simple, general method, which requires only small changes to existing models and software. We first form a joint Bayes ..read more
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Neural Networks and Deep Kernel Shaping
Department of Statistics | University of Oxford
by James Martens
2y ago
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping. Using an extended and formalized version of the Q/C map analysis of Pool et al. (2016), along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the "shape" of the network's initialization-time kernel function. We then develop a method called Deep Kernel Shaping (DKS), which accomplishes this using a combination ..read more
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Introduction to Advanced Research Computing at Oxford
Department of Statistics | University of Oxford
by Andy Gittings, Dai Jenkins
2y ago
Andy Gittings and Dai Jenkins, deliver a graduate lecture on Advance Research Computing (ARC ..read more
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Ethics from the perspective of an applied statistician
Department of Statistics | University of Oxford
by Denise Lievesley
2y ago
Professor Denise Lievesley discusses ethical issues and codes of conduct relevant to applied statisticians. Statisticians work in a wide variety of different political and cultural environments which influence their autonomy and their status, which in turn impact on the ethical frameworks they employ. The need for a UN-led fundamental set of principles governing official statistics became apparent at the end of the 1980s when countries in Central Europe began to change from centrally planned economies to market-oriented democracies. It was essential to ensure that national statistical systems ..read more
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A Day in the Life of a Statistics Consultant
Department of Statistics | University of Oxford
by Maria Christodoulou, Mariagrazia Zottoli
2y ago
Maria Christodoulou and Mariagrazia Zottoli share what a standard day is like for a statistics consultant ..read more
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Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo
Department of Statistics | University of Oxford
by Lionel Riou-Durand
2y ago
Lionel Riou-Durand gives a talk on sampling methods. Sampling approximations for high dimensional statistical models often rely on so-called gradient-based MCMC algorithms. It is now well established that these samplers scale better with the dimension than other state of the art MCMC samplers, but are also more sensitive to tuning. Among these, Hamiltonian Monte Carlo is a widely used sampling method shown to achieve gold standard d^{1/4} scaling with respect to the dimension. However it is also known that its efficiency is quite sensible to the choice of integration time. This problem is rela ..read more
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