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/

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/

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/

David Steinsaltz gives a lecture on the ethical issues in statistics using historical examples. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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 Bayesian model based upon the original submodels using a generic approach we call "Markov melding". We show that this model can be fitted in submodel-specific stages, rather than as a single, monolithic model. We also show the concept can be extended to "chains of submodels", in which submodels relate to neighbouring submodels via common quantities. The relationship to the "cut distribution" will also be discussed. We illustrate the approach using examples from an A/H1N1 influenza severity evidence synthesis; integrated population models in ecology; and modelling uncertain-time-to-event data in hospital intensive care units. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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 of precise parameter initialization, activation function transformations, and small architectural tweaks, all of which preserve the model class. In our experiments we show that DKS enables SGD training of residual networks without normalization layers on Imagenet and CIFAR-10 classification tasks at speeds comparable to standard ResNetV2 and Wide-ResNet models, with only a small decrease in generalization performance. And when using K-FAC as the optimizer, we achieve similar results for networks without skip connections. Our results apply for a large variety of activation functions, including those which traditionally perform very badly, such as the logistic sigmoid. In addition to DKS, we contribute a detailed analysis of skip connections, normalization layers, special activation functions like RELU and SELU, and various initialization schemes, explaining their effectiveness as alternative (and ultimately incomplete) ways of "shaping" the network's initialization-time kernel. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Andy Gittings and Dai Jenkins, deliver a graduate lecture on Advance Research Computing (ARC). Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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 in such countries would be able to produce appropriate and reliable data that adhered to certain professional and scientific standards. Alongside the UN initiative, a number of professional statistical societies adopted codes of conduct.

Do such sets of principles and ethical codes remain relevant over time? Or do changes in the way statistics are compiled and used mean that we need to review and adapt them? For example as combining data sources becomes more prevalent, record linkage, in particular, poses privacy and ethical challenges. Similarly obtaining informed consent from units for access to and linkage of their data from non-survey sources continues to be challenging. Denise draws on her earlier role as a statistician in the United Nations, working with some 200 countries, to discuss some of the ethical issues she encountered then and how these might change over time. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Maria Christodoulou and Mariagrazia Zottoli share what a standard day is like for a statistics consultant. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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 related to periodicity in the autocorrelations induced by the deterministic trajectories of Hamiltonian dynamics. To tackle this issue, we develop a robust alternative to HMC built upon Langevin diffusions (namely Metropolis Adjusted Langevin Trajectories, or MALT), inducing randomness in the trajectories through a continuous refreshment of the velocities. We study the optimal scaling problem for MALT and recover the d^{1/4} scaling of HMC without additional assumptions. Furthermore we highlight the fact that autocorrelations for MALT can be controlled by a uniform and monotonous bound thanks to the randomness induced in the trajectories, and therefore achieves robustness to tuning. Finally, we compare our approach to Randomized HMC and establish quantitative contraction rates for the 2-Wasserstein distance that support the choice of Langevin dynamics. This is a joint work with Jure Vogrinc, University of Warwick. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/