Search Results for author: Erik Waingarten

Found 5 papers, 0 papers with code

A Near-Linear Time Algorithm for the Chamfer Distance

no code implementations6 Jul 2023 Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten

For any two point sets $A, B \subset \mathbb{R}^d$ of size up to $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A, B)=\sum_{a \in A} \min_{b \in B} d_X(a, b)$, where $d_X$ is the underlying distance measure (e. g., the Euclidean or Manhattan distance).

Estimation of Entropy in Constant Space with Improved Sample Complexity

no code implementations19 May 2022 Maryam Aliakbarpour, Andrew Mcgregor, Jelani Nelson, Erik Waingarten

Recent work of Acharya et al. (NeurIPS 2019) showed how to estimate the entropy of a distribution $\mathcal D$ over an alphabet of size $k$ up to $\pm\epsilon$ additive error by streaming over $(k/\epsilon^3) \cdot \text{polylog}(1/\epsilon)$ i. i. d.

Learning and Testing Junta Distributions with Subcube Conditioning

no code implementations26 Apr 2020 Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten

The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning [BC18, CCKLW20].

Open-Ended Question Answering

Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

no code implementations17 Nov 2019 Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1, 1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)).

Approximate Near Neighbors for General Symmetric Norms

no code implementations18 Nov 2016 Alexandr Andoni, Huy L. Nguyen, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten

We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation.

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