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Riemannian optimization

Optimization methods on Riemannian manifolds.

Papers

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Showing 76–100 of 153 papers

TitleStatusHype
Tensor-Train Density EstimationCode1
Faster Randomized Methods for Orthogonality Constrained Problems—0
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian ApproachCode2
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein GeometryCode0
Accurate and fast matrix factorization for low-rank learningCode0
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds—0
Projection Robust Wasserstein Barycenters—0
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach—0
Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization—0
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient—0
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metricsCode0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
Riemannian stochastic recursive momentum method for non-convex optimization—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Variance reduction for Riemannian non-convex optimization with batch size adaptation—0
Projection Robust Wasserstein Distance and Riemannian Optimization—0
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection—0
Geoopt: Riemannian Optimization in PyTorchCode2
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Geomstats: A Python Package for Riemannian Geometry in Machine LearningCode2
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization—0
Affinity guided Geometric Semi-Supervised Metric Learning—0
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform—0
Communication-Efficient Distributed PCA by Riemannian Optimization—0
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference—0
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