基于神经模型的右删失观测优化
Neural Model-based Optimization with Right-Censored Observations
摘要：
在许多研究领域，我们只能观察到一些实验的真实响应值的下限。当拟合回归模型来预测结果的分布时，我们不能简单地丢弃这些右删失的观测值，而是需要对它们进行适当的建模。在这项工作中，我们专注于删失数据的概念，根据基于模型的优化，过早终止评估（从而生成右删失数据）是效率的关键因素，例如，当搜索最小化当前算法的运行时间的算法配置时。神经网络（NN）已被证明在基于模型的优化过程的核心工作，在这里，我们将其扩展到处理这些审查意见。我们提出（i）~一个损失函数的基础上的Tobit模型，将删失样本的训练和（ii）使用集成的网络模型的后验分布。然而，在优化开销方面是有效的，我们建议使用汤普森采样s.t.我们只需要在每次迭代中训练单个NN。我们的实验表明，我们训练的回归模型比几个基线实现了更好的预测质量，并且我们的方法在两个优化问题上实现了基于模型的优化的最新性能：最小化SAT求解器的求解时间和神经网络的准确时间。
英文摘要：
In many fields of study, we only observe lower bounds on the true response value of some experiments. When fitting a regression model to predict the distribution of the outcomes, we cannot simply drop these right-censored observations, but need to properly model them. In this work, we focus on the concept of censored data in the light of model-based optimization where prematurely terminating evaluations (and thus generating right-censored data) is a key factor for efficiency, e.g., when searching for an algorithm configuration that minimizes runtime of the algorithm at hand. Neural networks (NNs) have been demonstrated to work well at the core of model-based optimization procedures and here we extend them to handle these censored observations. We propose (i)~a loss function based on the Tobit model to incorporate censored samples into training and (ii) use an ensemble of networks to model the posterior distribution. To nevertheless be efficient in terms of optimization-overhead, we propose to use Thompson sampling s.t. we only need to train a single NN in each iteration. Our experiments show that our trained regression models achieve a better predictive quality than several baselines and that our approach achieves new state-of-the-art performance for model-based optimization on two optimization problems: minimizing the solution time of a SAT solver and the time-to-accuracy of neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
