Publications

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K Märtens, R Donovan-Maiye, J Ferkinghoff-Borg. Enhancing generative perturbation models with LLM-informed gene embeddings. Accepted to MLGenX workshop at ICLR 2024, 2024.

K Märtens, C Yau. Disentangling shared and private latent factors in multimodal Variational Autoencoders. Accepted to MLCB (2023), 2023.

Link to paper Code Video

K Märtens, C Yau. Neural Decomposition: Functional ANOVA with Variational Autoencoders. Accepted to AISTATS (2020), 2020.

Link to paper Code Slides Video

K Märtens, C Yau. BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders. Accepted to AISTATS (2020), 2020.

Link to paper Code Slides Video

K Märtens, K Campbell, C Yau. Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models. Accepted to ICML (2019), 2019.

Link to paper Code Poster Slides Video

K Märtens, MK Titsias, C Yau. Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. Accepted to AISTATS (2019), 2019.

Link to paper Code Poster

K Märtens, J Hallin, J Warringer, G Liti, L Parts. Predicting quantitative traits from genome and phenome with near perfect accuracy. In Nature communications (2016), 2016.

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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. In Bioinformatics (2016), 2016.

Link to paper Code Poster