Recent & Upcoming Talks

2020

Michael Lutter: Inductive Biases for Learning Robot Control

In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be …

2019

Vincent Adam: Doubly Sparse Variational Gaussian Processes

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their …

Samuel Kaski: Probabilistic Modelling with Experts

I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based …

Christian Walder: New Tricks for Estimating Gradients of Expectations

We derive a family of Monte Carlo estimators for gradients of expectations, which is related to the log-derivative trick, but involves …

Emtiyaz Khan: Learning-Algorithms from Bayesian Principle

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive …