论文标题
迭代域优化
Iterative Domain Optimization
论文作者
论文摘要
在本文中，我们研究了一种新的优化方法，该方法旨在搜索一个大域D，其中给定函数通过基于梯度的迭代优化算法进行大，小或特定的值。我们表明，所使用的目标函数无法直接优化，但是，我们使用一个技巧来近似每个迭代中的另一个目标以优化它。然后，我们在机器学习中探索该算法的用例，以查找模型在某些约束方面输出大小值的域。实验证明了该算法对五个病例的效率，并在泰坦尼克号数据集上训练有训练。
In this paper we study a new approach in optimization that aims to search a large domain D where a given function takes large, small or specific values via an iterative optimization algorithm based on the gradient. We show that the objective function used is not directly optimizable, however, we use a trick to approximate this objective by another one at each iteration to optimize it. Then we explore a use case of this algorithm in machine learning to find domains where the models output large and small values with respect of some constraints. Experiments demonstrate the efficiency of this algorithm on five cases with models trained on the titanic dataset.
