Search Results for author:
Found 5 papers, 0 papers with code
A Near-Linear Time Algorithm for the Chamfer Distance
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
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
The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning [BC18, CCKLW20].
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning
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
We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation.
