Online ensemble model compression for nonstationary data stream learning.

Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept drifts). However, the most common type of data stream learning approach are ensemble approaches, which involve the training of multiple base learners. This can severely increase their computational cost, especially when the learners have to recover from concept drift, rendering them inadequate for applications with tight time and space constraints. In this work, we propose Online Weight Averaging (OWA) - a robust and fast online model compression method for nonstationary data streams based on stochastic weight averaging. It is the first online model compression for nonstationary data streams, which is capable of compressing an evolving ensemble of neural networks into a single model continuously over time. It combines several snapshots of a neural network over time by averaging its weights in specific time steps to find promising regions in the loss landscape with the ability to forget weights from outdated time steps when a concept drift occurs. In this way, at any point in time, a single neural network is maintained to represent a whole ensemble, leveraging the power of ensembles while being appropriate for applications with tight speed requirements. Our experiments show that this key advantage of our proposed method also translates into other advantages such as (1) significant savings in computational cost compared to state-of-the-art data stream ensemble methods while (2) delivering similar predictive performance.