论文标题
随着时间变化的高斯工艺强盗优化，非恒定评估时间
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
论文作者
论文摘要
高斯过程匪徒是一个问题，我们希望找到具有最小功能评估数量的黑盒功能的最大化器。如果黑框函数随时间而变化，则随时间变化的贝叶斯优化是一个有希望的框架。但是，具有当前方法的缺点是假设每个观察值的评估时间都是恒定的，对于许多实际应用，例如推荐系统和环境监控，这可能是不现实的。结果，当违反此假设时，当前方法的性能可能会降低。为了解决这个问题，我们提出了一种新型时变贝叶斯优化算法，该算法可以有效地处理非恒定评估时间。此外，我们从理论上建立了算法的遗憾。我们的界限阐明了评估时间顺序的模式会严重影响问题的难度。我们还提供了实验结果来验证所提出方法的实际有效性。
The Gaussian process bandit is a problem in which we want to find a maximizer of a black-box function with the minimum number of function evaluations. If the black-box function varies with time, then time-varying Bayesian optimization is a promising framework. However, a drawback with current methods is in the assumption that the evaluation time for every observation is constant, which can be unrealistic for many practical applications, e.g., recommender systems and environmental monitoring. As a result, the performance of current methods can be degraded when this assumption is violated. To cope with this problem, we propose a novel time-varying Bayesian optimization algorithm that can effectively handle the non-constant evaluation time. Furthermore, we theoretically establish a regret bound of our algorithm. Our bound elucidates that a pattern of the evaluation time sequence can hugely affect the difficulty of the problem. We also provide experimental results to validate the practical effectiveness of the proposed method.
