Poster
in
Workshop: Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang · Bianca Dumitrascu · Sinead Williamson · Barbara Engelhardt
Abstract:
We propose a sequential Monte Carlo algorithm to fit infinite mixtures of GPs that capture non-stationary behavior while allowing for online, distributed inference. Our approach empirically improves performance over state-of-the-art methods for online GP estimation in the presence of non-stationarity in time-series data. To demonstrate the utility of our proposed online Gaussian process mixture-of-experts approach in applied settings, we show that we can successfully implement an optimization algorithm using online Gaussian process bandits.
Chat is not available.