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...
Jun 09, 2022•1 hr 4 min
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?
Apr 06, 2022•51 min
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?
Apr 05, 2022•53 min
David Steinsaltz gives a lecture on the ethical issues in statistics using historical examples.
Apr 05, 2022•56 min
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 me...
Apr 05, 2022•55 min
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 ...
Apr 05, 2022•55 min
Andy Gittings and Dai Jenkins, deliver a graduate lecture on Advance Research Computing (ARC).
Apr 05, 2022•49 min
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 eco...
Mar 31, 2022•40 min
Maria Christodoulou and Mariagrazia Zottoli share what a standard day is like for a statistics consultant.
Mar 31, 2022•40 min
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...
Mar 31, 2022•56 min
Professor Samir Bhatt gives a talk on the mathematics underpinning infectious disease models. Mathematical descriptions of infectious disease outbreaks are fundamental to understanding how transmission occurs. Reductively, two approaches are used: individual based simulators and governing equation models, and both approaches have a multitude of pros and cons. This talk connects these two worlds via general branching processes and discusses (at a high level) the rather beautiful mathematics that ...
Mar 31, 2022•59 min
Caroline Uhler (MIT), gives a OxCSML Seminar on Friday 2nd July 2021. Abstract: Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (genomics, advertisement, education, etc.). In order to obtain mechanistic insights from such data, a major challenge is the integration of different data modalities (video, audio, in...
Jul 29, 2021•59 min
Qiang Liu (University of Texas at Austin) gives the OxCSML Seminar on Friday 4th June 2021. Abstract: Stein's method is a powerful technique for deriving fundamental theoretical results on approximating and bounding distances between probability measures, such as central limit theorem. Recently, it was found that the key ideas in Stein's method, despite being originally designed as a pure theoretical technique, can be repurposed to provide a basis for developing practical and scalable computatio...
Jul 29, 2021•57 min
Cynthia Rudin (Duke University) gives a OxCSML Seminar on Friday 14th May 2021. Abstract: While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to understand by adding interpretability constraints. These constraints shrink the hypothesis space; that is, they make the model simpler. Statistical learning theor...
Jul 29, 2021•56 min
Aki Vehtari (Aalto University) gives the OxCSML Seminar on Friday 7th May 2021 Abstract: I discuss the use of the Pareto-k diagnostic as a simple and practical approach for estimating both the required minimum sample size and empirical pre-asymptotic convergence rate for Monte Carlo estimates. Even when by construction a Monte Carlo estimate has finite variance the pre-asymptotic behaviour and convergence rate can be very different from the asymptotic behaviour following the central limit theore...
Jul 29, 2021•58 min
Quan Zhou, Texas A and M University, gives an OxCSML Seminar on Friday 25th June 2021. Abstract: In a model selection problem, the size of the state space typically grows exponentially (or even faster) with p (the number of variables). But MCMC methods for model selection usually rely on local moves which only look at a neighborhood of size polynomial in p. Naturally one may wonder how efficient these sampling methods are at exploring the posterior distribution. Consider variable selection first...
Jul 02, 2021•1 hr 2 min
Distinguished Speaker Seminar - Friday 18th June 2021, with Susan Murphy, Professor of Statistics and Computer Science, Harvard John A. Paulson School of Engineering and Applied Sciences. Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in a Digital Health. However after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that t...
Jun 23, 2021•58 min
Graduate Lecture - Thursday 3rd June 2021, with Dr Fergus Boyles. Department of Statistics, University of Oxford. Drug discovery is a long and laborious process, with ever growing costs and dwindling productivity making it ever more difficult to bring new medicines to the market in an affordable and timely fashion. There is a long history of applying statistical modelling and machine learning to problems in drug discovery, and, as in many fields, there is growing excitement about the potential o...
Jun 23, 2021•57 min
OxCSML Seminar - Friday 28th May 2021, presented by Alexandra Carpentier (University of Magdeburg). In this talk we will discuss the thresholding bandit problem, i.e. a sequential learning setting where the learner samples sequentially K unknown distributions for T times, and aims at outputting at the end the set of distributions whose means \mu_k are above a threshold \tau. We will study this problem under four structural assumptions, i.e. shape constraints: that the sequence of means is monoto...
Jun 23, 2021•57 min
Benjamin Guedj, University College London, gives a OxCSML Seminar on 26th March 2021. Abstract: PAC-Bayes is a generic and flexible framework to address generalisation abilities of machine learning algorithms. It leverages the power of Bayesian inference and allows to derive new learning strategies. I will briefly present the key concepts of PAC-Bayes and highlight a few recent contributions from my group.
May 28, 2021•59 min
Julyan Arbel (Inria Grenoble - Rhône-Alpes), gives an OxCSML Seminar on Friday 30th April 2021, for the Department of Statistics.
May 21, 2021•57 min
Ben Lambert, Department of Computer Science, University of Oxford, gives the Graduate Lecture on Thursday 6th May 2021, for the Department of Statistics.
May 21, 2021•57 min
Professor Sara Van de Geer, ETH Zürich, gives the Distinguished Speaker Seminar on Thursday 29th April 2021 for the Department of Statistics.
May 21, 2021•1 hr
Murat Erdogdu gives the OxCSML Seminar on Friday 12th March, 2021, for the Department of Statistics.
May 21, 2021•1 hr 1 min
Karolina Dziugaite (Element AI), gives the OxCSML Seminar on 26th February 2021. Abstract: Deep learning approaches dominate in many application areas. Our understanding of generalization (relating empirical performance to future expected performance) is however lacking. In some applications, standard algorithms like stochastic gradient descent (SGD) reliably return solutions with low test error. In other applications, these same algorithms rapidly overfit. There is, as yet, no satisfying theory...
Mar 17, 2021•54 min
Professor Davina Durgana, award-winning international human rights statistician and professor with almost 15 years of experience developing leading global models to assess risk to modern slavery, gives a talk on their work on modern slavery. Abstract: Dr. Durgana will present her insights on the use of statistics in the global modern slavery vulnerability and prevalence field over the past decade. She will present work on the Global Estimates of Modern Slavery with the United Nations, Global Sla...
Mar 01, 2021•57 min
Bin Yu, Chancellor's Professor, Departments of Statistics and Electrical Engineering and Computer Science, UC Berkeley, gives a seminar for the Department of Statistics. 'A.I. is like nuclear energy - both promising and dangerous' - Bill Gates, 2019. Data Science is a pillar of A.I. and has driven most of recent cutting-edge discoveries in biomedical research. In practice, Data Science has a life cycle (DSLC) that includes problem formulation, data collection, data cleaning, modeling, result int...
Feb 26, 2021•1 hr 2 min
Professor Kerrie Mengersen, Distinguished Professor of Statistics at Queensland University of Technology in the Science and Engineering Faculty, gives the The Corcoran Memorial Lecture, held on 21st January 2021. Abstract: The ability to generate, access and combine multiple sources of data presents both opportunity and challenge for statistical science. An exemplar phenomenon is the charge to collate all relevant data for the purposes of comprehensive control and analysis. However, this ambitio...
Feb 05, 2021•1 hr 2 min
The Florence Nightingale Bicentennial Lecture was followed by a Panel Session with Professor Deborah Ashby, Professor David Cox and Professor David Spiegelhalter. The Panel was chaired by Professor Jennifer Rogers about the role of statistics in society
Feb 05, 2021•41 min
Professor Deborah Ashby, President of the RSS, gives the 2020 Florence Nightingale lecture. Florence Nightingale, best known as the Lady with the Lamp, is recognised as a pioneering and passionate statistician. She was also passionate about education, having argued successfully with her parents to be allowed to study mathematics, and later nursing, herself. More widely, she offered opinions on the education of children, soldiers, army doctors, and nurses, as well as railing against the ‘enforced...
Jan 07, 2021•39 min
