Search Results for author: Tyler Maunu

Found 10 papers, 1 papers with code

Stochastic and Private Nonconvex Outlier-Robust PCA

no code implementations17 Mar 2022 Tyler Maunu, Chenyu Yu, Gilad Lerman

Our results emphasize the advantages of the nonconvex methods over another convex approach to solving this problem in the differentially private setting.

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

1 code implementation13 Jan 2022 Yunpeng Shi, Shaohan Li, Tyler Maunu, Gilad Lerman

We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline.

Score-based Generative Neural Networks for Large-Scale Optimal Transport

no code implementations NeurIPS 2021 Max Daniels, Tyler Maunu, Paul Hand

We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions.

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence

no code implementations NeurIPS 2020 Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet

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 optimal transport.

Exponential ergodicity of mirror-Langevin diffusions

no code implementations NeurIPS 2020 Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme

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 in Zhang et al. (2020).

Depth Descent Synchronization in $\mathrm{SO}(D)$

no code implementations13 Feb 2020 Tyler Maunu, Gilad Lerman

We give robust recovery results for synchronization on the rotation group, $\mathrm{SO}(D)$.

Robust Subspace Recovery with Adversarial Outliers

no code implementations5 Apr 2019 Tyler Maunu, Gilad Lerman

The two estimators are fast to compute and achieve state-of-the-art theoretical performance in a noiseless RSR setting with adversarial outliers.

An Overview of Robust Subspace Recovery

no code implementations2 Mar 2018 Gilad Lerman, Tyler Maunu

This paper will serve as an introduction to the body of work on robust subspace recovery.

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

no code implementations13 Jun 2017 Tyler Maunu, Teng Zhang, Gilad Lerman

The practicality of the deterministic condition is demonstrated on some statistical models of data, and the method achieves almost state-of-the-art recovery guarantees on the Haystack Model for different regimes of sample size and ambient dimension.

Fast, Robust and Non-convex Subspace Recovery

no code implementations24 Jun 2014 Gilad Lerman, Tyler Maunu

Further, under a special model of data, FMS converges to a point which is near to the global minimum with overwhelming probability.

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