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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


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.

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