arXiv Machine Learning By Sanjeev Khanna, Ashwin Padaki, Erik Waingarten

Learning Partition Trees for Nearest Neighbor Search

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arXiv:2607. 09909v1 Announce Type: cross Abstract: We study nearest neighbor search from the perspective of data-driven algorithm design: given a dataset $P \subset \mathbb{R}^d$ of size $n$ and sample access to a query distribution over $\mathbb{R}^d$, the goal is to learn a data structure optimized for queries drawn from that specific distribution.

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arXiv Machine Learning
Jun 30

Actively Learning Halfspaces without Synthetic Data

arXiv:2509. 20848v2 Announce Type: replace-cross Abstract: In the classic point location problem, one is given an arbitrary dataset $X \subset \mathbb{R}^d$ of $n$ points with query access to an unknown halfspace $f : \mathbb{R}^d \to \{0,1\}$, and the goal is to learn the label of every point in $X$.

By Hadley Black, Kasper Green Larsen, Arya Mazumdar, Barna Saha, Geelon So
arXiv Machine Learning
Jul 10

High-Dimensional Procrustes Matching via Tree Counts

arXiv:2607. 08538v1 Announce Type: cross Abstract: Suppose we observe two sets of $n$ Gaussian vectors in $\mathbb{R}^d$, with the promise that, after applying a permutation of $[n]$ and a rotation of $\mathbb{R}^d$, the two sets are $\rho$-correlated.

By Xiaochun Niu, Tselil Schramm, Jiaming Xu
arXiv Machine Learning
Jul 31

Tight Bounds for Learning Polyhedra with a Margin

arXiv:2604. 14614v2 Announce Type: replace-cross Abstract: We give an algorithm for PAC learning intersections of $k$ halfspaces with a $\rho$ margin to within error $\varepsilon$ that runs in time $\textsf{poly}(k, \varepsilon^{-1}, \rho^{-1}) \cdot \exp \left(O(\sqrt{n \log(1/\rho) \log k})\right)$.

By Shyamal Patel, Santosh Vempala