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arXiv · 2006.05468

Variational Auto-Regressive Gaussian Processes for Continual Learning

Sanyam Kapoor·Theofanis Karaletsos·Thang D. Bui
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Abstract

Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.

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Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui. 2020-06-09. Variational Auto-Regressive Gaussian Processes for Continual Learning. https://arxiv.org/abs/2006.05468

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