conftrace_

stiefel manifold

29 papers

Explore in graph

Co-occurring keywords

riemannian optimization (89) manifold optimization (67) orthogonality constraint (18) gradient descent (1144) stochastic gradient descent (1091) optimal transport (948) stochastic optimization (1060) wasserstein distance (417) orthogonal constraint (19) graph neural network (3962)

Papers

A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting IJCAI 2025
Riemannian Optimization for LoRA on the Stiefel Manifold EMNLP 2025
Riemannian Geometric-based Meta Learning AAAI 2025
Simple Orthogonal Graph Representation Learning (Student Abstract) AAAI 2024
Personalized PCA: Decoupling Shared and Unique Features JMLR 2024
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints JMLR 2024
Chordal Averaging on Flag Manifolds and Its Applications ICCV 2023
A Riemannian Exponential Augmented Lagrangian Method for Computing the Projection Robust Wasserstein Distance NIPS 2023
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold COLT 2023
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems AAAI 2023
Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold AAAI 2022
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs AAAI 2022
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold JMLR 2022
Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation NIPS 2021
On the Riemannian Search for Eigenvector Computation JMLR 2021
Projection Robust Wasserstein Barycenters ICML 2021
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices AISTATS 2021
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance ICML 2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold ICML 2021
Expressiveness and Learning of Hidden Quantum Markov Models AISTATS 2020
A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace UAI 2020
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint AAAI 2020
Continual Learning in Low-rank Orthogonal Subspaces NIPS 2020
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data NIPS 2019
Non-smooth Optimization over Stiefel Manifolds with Applications to Dimensionality Reduction and Graph Clustering IJCAI 2019