Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms

23 Feb 2018Ilias DiakonikolasJerry LiLudwig Schmidt

We study the problem of robustly learning multi-dimensional histograms. A $d$-dimensional function $h: D \rightarrow \mathbb{R}$ is called a $k$-histogram if there exists a partition of the domain $D \subseteq \mathbb{R}^d$ into $k$ axis-aligned rectangles such that $h$ is constant within each such rectangle... (read more)

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