Best Zero Supervision Podcasts (2025)

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A Theory of Weak-Supervision and Zero-Shot Learning 1:03:33

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3+ y ago1:03:33

1:03:33

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 th…

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Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction 50:33

3+ y ago50:33

50:33

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 i…

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The practicalities of academic research ethics - how to get things done 52:45

3+ y ago52:45

52:45

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 d…

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Statistics, ethical and unethical: Some historical vignettes 56:11

3+ y ago56:11

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David Steinsaltz gives a lecture on the ethical issues in statistics using historical examples.By David Steinsaltz

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Joining Bayesian submodels with Markov melding 55:11

3+ y ago55:11

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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 ty…

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Neural Networks and Deep Kernel Shaping 55:17

3+ y ago55:17

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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 general…

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Introduction to Advanced Research Computing at Oxford 48:40

3+ y ago48:40

48:40

Andy Gittings and Dai Jenkins, deliver a graduate lecture on Advance Research Computing (ARC).By Andy Gittings, Dai Jenkins

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Ethics from the perspective of an applied statistician 39:49

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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 princi…

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A Day in the Life of a Statistics Consultant 40:19

3+ y ago40:19

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Maria Christodoulou and Mariagrazia Zottoli share what a standard day is like for a statistics consultant.By Maria Christodoulou, Mariagrazia Zottoli

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Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo 56:00

3+ y ago56:00

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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, Ham…

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Modelling infectious diseases: what can branching processes tell us? 59:22

3+ y ago59:22

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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 pro…

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Causality and Autoencoders in the Light of Drug Repurposing for COVID-19 58:58

4y ago58:58

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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, educati…

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Recent Applications of Stein's Method in Machine Learning 56:43

4y ago56:43

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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, de…

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Do Simpler Models Exist and How Can We Find Them? 56:01

4y ago56:01

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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 b…

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Practical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learning 57:48

4y ago57:48

57:48

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 fi…

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Complexity of local MCMC methods for high-dimensional model selection 1:01:51

4y ago1:01:51

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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 polynomi…

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Assessing Personalization in Digital Health 58:20

4+ y ago58:20

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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 …

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Machine Learning in Drug Discovery 56:49

4+ y ago56:49

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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 applyi…

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Several structured thresholding bandit problems 57:14

4+ y ago57:14

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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 …

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A primer on PAC-Bayesian learning followed by News from the PAC-Bayes frontline 59:06

4+ y ago59:06

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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-Ba…

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Approximate Bayesian computation with surrogate posteriors 56:42

4+ y ago56:42

56:42

Julyan Arbel (Inria Grenoble - Rhône-Alpes), gives an OxCSML Seminar on Friday 30th April 2021, for the Department of Statistics.By Julyan Arbel

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Introduction to Bayesian inference for Differential Equation Models Using PINTS 57:10

4+ y ago57:10

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Ben Lambert, Department of Computer Science, University of Oxford, gives the Graduate Lecture on Thursday 6th May 2021, for the Department of Statistics.By Ben Lambert

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On classification with small Bayes error and the max-margin classifier 1:00:01

4+ y ago1:00:01

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Professor Sara Van de Geer, ETH Zürich, gives the Distinguished Speaker Seminar on Thursday 29th April 2021 for the Department of Statistics.By Sara Van de Geer

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Convergence of Online SGD under Infinite Noise Variance, and Non-convexity 1:00:40

4+ y ago1:00:40

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Murat Erdogdu gives the OxCSML Seminar on Friday 12th March, 2021, for the Department of Statistics.By Murat Erdogdu

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Distribution-dependent generalization bounds for noisy, iterative learning algorithms 54:09

4+ y ago54:09

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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 (SG…

Zero Supervision Podcasts

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Zero Supervision Comics

Zero Supervision

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Department of Statistics

Oxford University

1
A Theory of Weak-Supervision and Zero-Shot Learning 1:03:33

1
Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction 50:33

1
The practicalities of academic research ethics - how to get things done 52:45

1
Statistics, ethical and unethical: Some historical vignettes 56:11

1
Joining Bayesian submodels with Markov melding 55:11

1
Neural Networks and Deep Kernel Shaping 55:17

1
Introduction to Advanced Research Computing at Oxford 48:40

1
Ethics from the perspective of an applied statistician 39:49

1
A Day in the Life of a Statistics Consultant 40:19

1
Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo 56:00

1
Modelling infectious diseases: what can branching processes tell us? 59:22

1
Causality and Autoencoders in the Light of Drug Repurposing for COVID-19 58:58

1
Recent Applications of Stein's Method in Machine Learning 56:43

1
Do Simpler Models Exist and How Can We Find Them? 56:01

1
Practical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learning 57:48

1
Complexity of local MCMC methods for high-dimensional model selection 1:01:51

1
Assessing Personalization in Digital Health 58:20

1
Machine Learning in Drug Discovery 56:49

1
Several structured thresholding bandit problems 57:14

1
A primer on PAC-Bayesian learning followed by News from the PAC-Bayes frontline 59:06

1
Approximate Bayesian computation with surrogate posteriors 56:42

1
Introduction to Bayesian inference for Differential Equation Models Using PINTS 57:10

1
On classification with small Bayes error and the max-margin classifier 1:00:01

1
Convergence of Online SGD under Infinite Noise Variance, and Non-convexity 1:00:40

1
Distribution-dependent generalization bounds for noisy, iterative learning algorithms 54:09