These are some of the talks I’ve given in the past:
Gaussian processes for fun and profit: Probabilistic machine learning in industry
Abstract: When companies, whether start-ups or big corporations, talk about “machine learning” they usually mean some kind of neural network model. Not always though: I will talk about why instead we put a lot of our efforts on probabilistic models built using Gaussian processes. When a Machine Learning course briefly covers Gaussian processes, you might go away thinking they’re just basis function interpolation, only apply when the noise is Gaussian, and don’t scale to larger datasets. Here I will discuss why these are misconceptions and show why Gaussian processes are both interesting and useful in practical applications.
Multiple dispatch in the inducing variable and multi-output framework in GPflow
This was a talk about the importance of good software abstractions in writing composable, re-usable research code, and how we make use of multiple dispatch to achieve this in GPflow.
[ Slides ]
Invariances in Gaussian processes and how to learn them
Abstract: When learning mappings from data, knowledge about what modifications to the input leave the output unchanged can strongly improve generalisation. Exploiting these invariances is commonplace in many machine learning models, under the guise of convolutional structure or data augmentation. Choosing which invariances to use, however, is still done with humans in the loop, through trial-and-error and crossvalidation. In this talk, we will discuss how Gaussian processes can be constrained to exhibit invariances, and how this is useful for various applications. We will also show how invariances can be learned with backpropagation using tools from Bayesian model selection.
Jointly with Mark van der Wilk.
[ Slides ]
Bayesian modelling of large-scale spatiotemporal discrete events: Gaussian processes, Cox processes and Fourier features
Infectious Disease Epidemiology Seminar Series, Imperial College London, 2018
Abstract: Bayesian inference allows us to build probabilistic models in which we derive not just point estimates, but also uncertainty quantification from our observed data. An underrepresented area in this field of research is the modelling of point processes: discrete events such as disease cases in epidemiology, locations of trees in ecology or distribution of taxi pickup requests in smart cites. I will talk about how we can use Gaussian processes to infer the rate function that describes the intensity of events across the spatiotemporal domain in a Cox process. I will give an overview over different approaches and their advantages and disadvantages, including recent work on using Fourier features (a representation of the function in the spectral domain) to scale up point process inference to larger data sets.
[ Slides ]
Many-body coarse grained interactions using Gaussian approximation potentials.
Abstract: We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a cluster expansion in terms of monomer, dimer, and trimer terms. The contributions to the free energy due to these terms are inferred from all-atom molecular dynamics (MD) data using Gaussian Approximation Potentials, a type of machine-learning model that employs Gaussian process regression. The resulting CG model is much more accurate than those possible using pair potentials. Though slower than the latter, our model can still be faster than all-atom simulations for solvent-free CG models commonly used in biomolecular simulations.
[ Slides ]