120 citations · 273 across the 13 of their papers we have counts for
13 papers
Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
Gilad Lerman, Kang Li, Tyler Maunu +1
Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective appr…
Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery
Tyler Maunu, Thibaut Le Gouic, Philippe Rigollet
We revisit the problem of recovering a low-rank positive semidefinite matrix from rank-one projections using tools from optimal transport. More specifically, we show that a variati…
Stochastic and Private Nonconvex Outlier-Robust PCA
Tyler Maunu, Chenyu Yu, Gilad Lerman
We develop theoretically guaranteed stochastic methods for outlier-robust PCA. Outlier-robust PCA seeks an underlying low-dimensional linear subspace from a dataset that is corrupt…
Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching
Yunpeng Shi, Shaohan Li, Tyler Maunu +1
We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline. The statistics are based on consistency constraints that arise wi…
Score-based Generative Neural Networks for Large-Scale Optimal Transport
Mara Daniels, Tyler Maunu, Paul Hand
We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions. In certain cases, the optimal transport plan takes the…
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Sinho Chewi, Thibaut Le Gouic, Chen Lu +2
Stein Variational Gradient Descent (SVGD), a popular sampling algorithm, is often described as the kernelized gradient flow for the Kullback-Leibler divergence in the geometry of o…
Exponential ergodicity of mirror-Langevin diffusions
Sinho Chewi, Thibaut Le Gouic, Chen Lu +3
Motivated by the problem of sampling from ill-conditioned log-concave distributions, we give a clean non-asymptotic convergence analysis of mirror-Langevin diffusions as introduced…
Depth Descent Synchronization in
Tyler Maunu, Gilad Lerman
We give robust recovery results for synchronization on the rotation group, . In particular, we consider an adversarial corruption setting, where a limited percentag…
Gradient descent algorithms for Bures-Wasserstein barycenters
Sinho Chewi, Tyler Maunu, Philippe Rigollet +1
We study first order methods to compute the barycenter of a probability distribution over the space of probability measures with finite second moment. We develop a framework to…
Robust Subspace Recovery with Adversarial Outliers
Tyler Maunu, Gilad Lerman
We study the problem of robust subspace recovery (RSR) in the presence of adversarial outliers. That is, we seek a subspace that contains a large portion of a dataset when some fra…
An Overview of Robust Subspace Recovery
Gilad Lerman, Tyler Maunu
This paper will serve as an introduction to the body of work on robust subspace recovery. Robust subspace recovery involves finding an underlying low-dimensional subspace in a data…
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
Tyler Maunu, Teng Zhang, Gilad Lerman
We present a mathematical analysis of a non-convex energy landscape for robust subspace recovery. We prove that an underlying subspace is the only stationary point and local minimi…
Fast, Robust and Non-convex Subspace Recovery
Gilad Lerman, Tyler Maunu
This work presents a fast and non-convex algorithm for robust subspace recovery. The data sets considered include inliers drawn around a low-dimensional subspace of a higher dimens…