37 citations · 48 across the 9 of their papers we have counts for
11 papers
Functional Multi-Target Detection via Bispectrum Inversion
Anna Little, Daniel Sanz-Alonso, Mikhail Sweeney +1
This paper develops a functional theory for multi-target detection, where a compactly supported signal is recovered from a single noisy observation containing many unknown translat…
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms
Nicolás García Trillos, Anna Little, Daniel McKenzie +1
We analyze the convergence properties of Fermat distances, a family of density-driven metrics defined on Riemannian manifolds with an associated probability measure. Fermat distanc…
Linear Distance Metric Learning with Noisy Labels
Meysam Alishahi, Anna Little, Jeff M. Phillips
In linear distance metric learning, we are given data in one Euclidean metric space and the goal is to find an appropriate linear map to another Euclidean metric space which respec…
On Generalizations of the Nonwindowed Scattering Transform
Albert Chua, Matthew Hirn, Anna Little
In this paper, we generalize finite depth wavelet scattering transforms, which we formulate as $\Lb^q(\mathbb{R}^n)$ norms of a cascade of continuous wavelet transforms (or dyadic…
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel +9
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on de…
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Unbiasing Procedures for Scale-invariant Multi-reference Alignment
Matthew Hirn, Anna Little
This article discusses a generalization of the 1-dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each nois…
Balancing Geometry and Density: Path Distances on High-Dimensional Data
Anna Little, Daniel McKenzie, James Murphy
New geometric and computational analyses of power-weighted shortest-path distances (PWSPDs) are presented. By illuminating the way these metrics balance density and geometry in the…
Wavelet invariants for statistically robust multi-reference alignment
Matthew Hirn, Anna Little
We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference a…
Exact Cluster Recovery via Classical Multidimensional Scaling
Anna Little, Yuying Xie, Qiang Sun
Classical multidimensional scaling is an important dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides…
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Anna Little, Mauro Maggioni, James M. Murphy
We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters. We prove finite-sample g…