In this year’s ICML, some interesting work was presented on Neural Processes. In this blog post, I discuss what Neural Processes are and how they behave as a prior over functions.
I am a PhD student in Statistical Machine Learning at the University of Oxford, as part of the OxCSML group in the Department of Statistics. I am supervised by Christopher Yau and Chris Holmes.
I have a broad interest in statistical machine learning and Bayesian inference techniques with a focus on tackling real problems in genomics and healthcare. My PhD research focuses on developing probabilistic latent variable models (such as Gaussian Process Latent Variable Models and Variational AutoEncoders) with applications to biomedical data.
I am also enthusiastic about R and data visualisation, and I co-organise the Oxford R user group meetups. Additionally, in my research I like to make use of automatic differentiation frameworks (such as TensorFlow and PyTorch) and I am enthusiastic about probabilistic programming frameworks (such as Stan, PyMC3, or Edward).
Prior to the PhD, I studied for BSc and MSc at the University of Tartu in Estonia. There I was part of the BIIT research group at the Institute of Computer Science, where I worked on statistical modelling in genomics under the supervision of Raivo Kolde and Leopold Parts.
In this year’s ICML, some interesting work was presented on Neural Processes. In this blog post, I discuss what Neural Processes are and how they behave as a prior over functions.
NUTS implementation in R
Rcpp implementation for DP and MFM mixtures
R package implementing the Polya-Gamma augmentation scheme
Here you can find course material (in Estonian!) on Data Science and Visualisation, which we created together with Tanel Pärnamaa. This course “Statistiline andmeteadus ja visualiseerimine” is centered around a number of interesting case studies, and it focuses on teaching good practices of data science in R by applying statistical methods to solve these real-life problems.
