Tom Rainforth

Probabilistic structure discovery in time series data

2016 · arXiv (Cornell University)

Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned …

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

2021 · International Conference on Machine Learning

We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and via an extensive empirical evaluation …