Bayesian ML and Probabilistic Programming
When the understanding of phenomena and interpretation of results are required in industrial or scientific applications, black-box models give way to hand-crafted (or automatically discovered) probabilistic models. Classical Bayesian methods do not scale well to high dimensions or large datasets and are plagued by intractable computations. But with the development of expressive and elegant probabilistic programming languages like Stan or Pyro and their incorporation of advanced approximate inference methods, today’s practitioners benefit from a powerful arsenal that enables faster development of more complex models than ever before.

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Spectral likelihood expansions for Bayesian inference

Spectral Likelihood expansion represents the Likelihood using Polynomial Chaos Expansion to speed up access to the posterior. It thus …

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Bayesian ML and Probabilistic Programming
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Statistical Rethinking

A beautiful lecture series on Bayesian statistics.

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Bayesian ML and Probabilistic Programming
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Natural Posterior Networks

A new way to train neural networks using the exponential family for estimates of predictive uncertainty.

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Bayesian ML and Probabilistic Programming
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Variational Methods for Simulation Based Inference

A new framework for likelihood-free inference, i.e. the task of using probabilistic / Bayesian Methods in the absence of tractable …

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Bayesian ML and Probabilistic Programming
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Scalable Bayesian Deep Learning with Modern Laplace Approximations

Training Bayesian neural networks using the Laplacian method for Gaussian posterior approximation.

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Bayesian ML and Probabilistic Programming
Training

An introduction to Bayesian methods in ML

A two-day workshop introducing Bayesian modelling, using practical examples and probabilistic programming.

Seminar

Hamiltonian Monte Carlo Sampling

Metropolis, Slice or Gibbs sampling tend to be inefficient when applied in high dimensions due to their local random walk behaviour. …

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Bayesian ML and Probabilistic Programming
Seminar

Bayesian ML and Probabilistic Programming 3: Variational Inference

In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the …

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Bayesian ML and Probabilistic Programming
Seminar

Bayesian ML and Probabilistic Programming 2: MCMC, Bayesian Regression and Causality

In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the …

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Bayesian ML and Probabilistic Programming
Seminar

Bayesian ML and Probabilistic Programming: Introduction

In this seminar, we go back to the very basics with Surya, who will (re)visit the fundamentals of the Bayesian approach to ML, including …

Surya Kocherlakota
Bayesian ML and Probabilistic Programming

Other series in Trustworthy and interpretable ML

Classifier calibration

For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one says that the classifier is calibrated). Recently, it has been shown that common models fail to satisfy …

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Explainable AI

Large opaque models like neural networks require dedicated methods to study and interpret their behavior. In this series we review recent developments, analysing their relevance for business applications.

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Simulation-based Inference

Computer simulations provide high-fidelity models and allow to generate insights without the need of running real experiments. They are especially useful in areas where real experiments would be too costly or even impossible. However, …

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