Doctoral Symposium Talk: Kaspar Märtens

Abstract: Kaspar Märtens's (University of Oxford, Expected 2020) research focuses on enabling feature-level interpretability in non-linear latent variable models via a synthesis of statistical and machine learning techniques. In particular, Kaspar designs novel latent variable, non-linear dimensionality reduction models that allow for feature-level interpretability, focusing primarily on gaussian process latent variable models (GPLVMs) and variational autoencoders (VAEs), specifically augmenting these models with ideas from classical statistics, such as the functional analysis of variance (ANOVA) decomposition or probabilistic clustering algorithms. The results of these works are a class of models for flexible non-linear dimensionality reduction together with explainability, providing a mechanism to gain insights into what the model has learnt in terms of the observed features. In other work, Kaspar has examined genomic problems and applications of MCMC sampling.

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