Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning challenging. Here, we present a method to keep all such errors in check using the recently proposed dual sparse variational GP. Our method enables accurate inference for generic likelihoods and improves learning by actively building and updating a memory of past data. We demonstrate its effectiveness in several applications involving Bayesian optimization, active learning, and continual learning.

翻译：序贯学习中，当过去数据的访问受限时（例如连续学习和主动学习场景），高斯过程的应用面临挑战。由于后验分布、超参数和诱导点存在不准确性，误差会随时间累积，导致精确学习困难。本文提出一种利用近期提出的双稀疏变分高斯过程控制所有此类误差的方法。该方法能够针对通用似然函数实现精确推断，并通过主动构建和更新过去数据记忆来提升学习效果。我们在涉及贝叶斯优化、主动学习和连续学习的多项应用中验证了其有效性。