Kaspar Märtens

Kaspar Märtens

Statistical machine learning, probabilistic inference, deep generative models, Gaussian Processes

Kaspar is a DPhil student at the Department of Statistics, University of Oxford, supervised by Christopher Yau and Chris Holmes.

He has a broad interest in statistical machine learning and Bayesian inference techniques, with a focus on tackling real problems in genomics and healthcare. His PhD research focuses on developing probabilistic latent variable models (such as Gaussian Process Latent Variable Models and Variational Autoencoders) with applications to biomedical data.

Kaspar is also an R enthusiast and organises the Oxford R user group meetings.



  • K. Märtens , C. Yau , BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  • K. Märtens , C. Yau , Neural Decomposition: Functional ANOVA with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.


  • K. Märtens , K. Campbell , C. Yau , Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models, in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, vol. 97, 4372–4381.
  • K. Märtens , M. Titsias , C. Yau , Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2019, vol. 89, 2359–2367.


  • K. Märtens , J. Hallin , J. Warringer , G. Liti , L. Parts , Predicting quantitative traits from genome and phenome with near perfect accuracy, Nature Communications, vol. 7, 11512, 2016.
  • J. Hallin , K. Märtens , A. I. Young , M. Zackrisson , F. Salinas , L. Parts , J. Warringer , G. Liti , Powerful decomposition of complex traits in a diploid model, Nature Communications, vol. 7, 13311, 2016.
  • R. Kolde , K. Märtens , K. Lokk , S. Laur , J. Vilo , seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data, Bioinformatics, vol. 32, no. 17, 2604–2610, 2016.


  • K. Lokk , V. Modhukur , B. Rajashekar , K. Märtens , R. Mägi , R. Kolde , M. Koltšina , T. K. Nilsson , J. Vilo , A. Salumets , . others , DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns, Genome Biology, vol. 15, no. 4, 3248, 2014.