Gaussian Processes and Bayesian Inference

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing

The paper establishes the relationship between Gaussian process priors and regularized regression in sequence-to-function mapping, showing how to construct regularizers that correspond to explicit Gaussian process priors and characterize implicit function space priors.

Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

This paper introduces a Gaussian process multi-class decoder (GPMD) for neural decoding, achieving state-of-the-art accuracy across species and demonstrating the importance of correlation structure.

Variational Log-Gaussian Point-Process Methods for Grid Cells

The paper develops variational log-Gaussian point-process methods to efficiently calculate spatial statistics for grid cells in large environments using Gaussian processes.

Scalable gaussian process inference of neural responses to natural images

This paper develops a scalable Gaussian process model for predicting neural responses to natural images, outperforming deep neural networks on small datasets and enabling efficient model learning and comparison.

Scalable variational inference for low-rank spatio-temporal receptive fields

The paper introduces a scalable variational inference method for estimating low-rank spatio-temporal receptive fields in neural data, showing improved performance over existing estimators.

Warped Bayesian Linear Regression for Normative Modelling of Big Data

This paper introduces a warped Bayesian Linear Regression framework for normative modelling, improving scalability and non-Gaussian distribution handling in neuroimaging data.

The pitfalls of using Gaussian Process Regression for normative modeling

This paper shows that Gaussian Process Regression uncertainty is not relevant for quantifying cohort heterogeneity in normative modeling.

Bayesian Hypothesis Testing And Experimental Design For Two-Photon Imaging Data

The paper develops a Bayesian statistical pipeline for analyzing two-photon imaging data using Gaussian Process Regression to infer neural activity and guide experimental design.

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series

This paper introduces branch-recombinant Gaussian processes (B-RGPs) to analyze branching and recombination in biological time series, demonstrating their application to gene expression data in Arabidopsis thaliana.