论文标题
通过Delaunay改进的活跃最近的邻居回归
Active Nearest Neighbor Regression Through Delaunay Refinement
论文作者
论文摘要
我们引入了一种基于最近的邻居回归的活动函数近似的算法。我们活跃的邻居回归器（ANNR）依赖于从计算几何形状到将空间细分为具有恒定估计函数值的细胞的Voronoi-Delaunay框架，并以将功能图的几何形状考虑在内的方式中选择新的查询点。我们将最新的最新活动函数近似值（称为defer）视为基于空间的增量矩形分区，为主基线。 ANNR解决了延期中使用的空间细分策略产生的许多局限性。我们提供了对方法的计算有效实施，以及理论停止保证。经验结果表明，Annr优于封闭形式函数和现实示例的基线，例如引力波参数推断和生成模型潜在空间的探索。
We introduce an algorithm for active function approximation based on nearest neighbor regression. Our Active Nearest Neighbor Regressor (ANNR) relies on the Voronoi-Delaunay framework from computational geometry to subdivide the space into cells with constant estimated function value and select novel query points in a way that takes the geometry of the function graph into account. We consider the recent state-of-the-art active function approximator called DEFER, which is based on incremental rectangular partitioning of the space, as the main baseline. The ANNR addresses a number of limitations that arise from the space subdivision strategy used in DEFER. We provide a computationally efficient implementation of our method, as well as theoretical halting guarantees. Empirical results show that ANNR outperforms the baseline for both closed-form functions and real-world examples, such as gravitational wave parameter inference and exploration of the latent space of a generative model.
