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Riemannian optimization

Optimization methods on Riemannian manifolds.

Papers

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Showing 51–100 of 153 papers

TitleStatusHype
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency AnalysisCode1
Learning Graphical Factor Models with Riemannian OptimizationCode0
Rieoptax: Riemannian Optimization in JAXCode0
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian ManifoldCode0
Manifold Free Riemannian Optimization—0
Riemannian optimization for non-centered mixture of scaled Gaussian distributionsCode0
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints—0
On a class of geodesically convex optimization problems solved via Euclidean MM methods—0
Riemannian CUR Decompositions for Robust Principal Component Analysis—0
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their InterplayCode0
Riemannian stochastic approximation algorithms—0
Differentially private Riemannian optimization—0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Riemannian Hamiltonian methods for min-max optimization on manifolds—0
Riemannian block SPD coupling manifold and its application to optimal transportCode0
Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System—0
Understanding Riemannian Acceleration via a Proximal Extragradient Framework—0
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization—0
Projective Manifold Gradient Layer for Deep Rotation RegressionCode1
Learning with symmetric positive definite matrices via generalized Bures-Wasserstein geometryCode0
Tensor train completion: local recovery guarantees via Riemannian optimization—0
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems—0
Nonlinear matrix recovery using optimization on the Grassmann manifoldCode0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix ManifoldsCode0
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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