no code implementations • 24 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.
no code implementations • 13 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.
no code implementations • 2 Mar 2018 • Gilad Lerman, Tyler Maunu
This paper will serve as an introduction to the body of work on robust subspace recovery.
no code implementations • 5 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.
no code implementations • 13 Feb 2020 • Tyler Maunu, Gilad Lerman
We give robust recovery results for synchronization on the rotation group, $\mathrm{SO}(D)$.
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).
1 code implementation • 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.
1 code implementation • 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.
1 code implementation • 13 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.
no code implementations • 17 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.
no code implementations • 21 Nov 2023 • Tyler Maunu, Martin Molina-Fructuoso
We study accelerated optimization methods in the Gaussian phase retrieval problem.