Search Results for author:
Found 11 papers, 3 papers with code
Acceleration and Implicit Regularization in Gaussian Phase Retrieval
We study accelerated optimization methods in the Gaussian phase retrieval problem.
Stochastic and Private Nonconvex Outlier-Robust PCA
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
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
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
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
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 $\mathrm{SO}(D)$
We give robust recovery results for synchronization on the rotation group, $\mathrm{SO}(D)$.
Robust Subspace Recovery with Adversarial Outliers
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
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
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
Further, under a special model of data, FMS converges to a point which is near to the global minimum with overwhelming probability.
