Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds

23 May 2016

Papers citing "Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds"

50 / 118 papers shown

Title
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method Andi Han Pierre-Louis Poirion Akiko Takeda 2 0 0 18 May 2025
SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges Ce Ju Reinmar J. Kobler Antoine Collas M. Kawanabe Cuntai Guan Bertrand Thirion 43 0 0 26 Apr 2025
Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries Neil He Jiahong Liu Buze Zhang N. Bui Ali Maatouk Menglin Yang Irwin King Melanie Weber Rex Ying 29 0 0 11 Apr 2025
Rethinking RoPE: A Mathematical Blueprint for N-dimensional Positional Encoding Haiping Liu Hongpeng Zhou AI4CE 31 0 0 07 Apr 2025
$k$ -SVD with Gradient Descent Emily Gan Yassir Jedra Devavrat Shah 66 0 0 01 Feb 2025
Structured Regularization for Constrained Optimization on the SPD Manifold Andrew Cheng Melanie Weber 11 1 0 12 Oct 2024
Riemannian Federated Learning via Averaging Gradient Stream Zhenwei Huang Wen Huang Pratik Jawanpuria Bamdev Mishra FedML 35 1 0 11 Sep 2024
Disciplined Geodesically Convex Programming Andrew Cheng Vaibhav Dixit Melanie Weber 18 1 0 07 Jul 2024
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds Daniel Dodd Louis Sharrock Christopher Nemeth 41 0 0 04 Jun 2024
Riemannian coordinate descent algorithms on matrix manifolds Andi Han Pratik Jawanpuria Bamdev Mishra 48 5 0 04 Jun 2024
Riemannian Optimization for Active Mapping with Robot Teams Arash Asgharivaskasi Fritz Girke Nikolay Atanasov 34 3 0 28 Apr 2024
Federated Learning on Riemannian Manifolds with Differential Privacy Zhenwei Huang Wen Huang Pratik Jawanpuria Bamdev Mishra FedML 39 6 0 15 Apr 2024
FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching Rohit Jena Pratik Chaudhari James C. Gee 52 2 0 01 Apr 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction Yury Demidovich Grigory Malinovsky Peter Richtárik 56 2 0 11 Mar 2024
A Framework for Bilevel Optimization on Riemannian Manifolds Andi Han Bamdev Mishra Pratik Jawanpuria Akiko Takeda 21 4 0 06 Feb 2024
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent Benjamin Gess Sebastian Kassing Nimit Rana 40 0 0 02 Feb 2024
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space Mingyang Yi Bohan Wang 32 0 0 24 Jan 2024
The Fisher-Rao geometry of CES distributions Florent Bouchard A. Breloy Antoine Collas Alexandre Renaux G. Ginolhac 18 5 0 02 Oct 2023
RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization Kenneth Allen Hoang-Phi Nguyen Tung Pham Ming-Jun Lai Mehrtash Harandi Dinh Q. Phung Trung Le AAML 40 3 0 29 Sep 2023
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms Jiaxiang Li Krishnakumar Balasubramanian Shiqian Ma 19 2 0 25 Sep 2023
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds Yong Cai Michael I. Jordan Tianyi Lin Argyris Oikonomou Emmanouil-Vasileios Vlatakis-Gkaragkounis 30 4 0 29 Jun 2023
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties David Martínez-Rubio Christophe Roux Christopher Criscitiello Sebastian Pokutta 22 6 0 25 May 2023
Low-complexity subspace-descent over symmetric positive definite manifold Yogesh Darmwal K. Rajawat 44 3 0 03 May 2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints Pierre Ablin Simon Vary Bin Gao P.-A. Absil 54 7 0 29 Mar 2023
Convergence of variational Monte Carlo simulation and scale-invariant pre-training Nilin Abrahamsen Zhiyan Ding Gil Goldshlager Lin Lin DRL 35 2 0 21 Mar 2023
Particle-based Online Bayesian Sampling Yifan Yang Chang-rui Liu Zhengze Zhang BDL 19 7 0 28 Feb 2023
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization Yian Deng Tingting Mu 21 1 0 22 Feb 2023
Constrained Empirical Risk Minimization: Theory and Practice Eric Marcus Ray Sheombarsing J. Sonke Jonas Teuwen 15 1 0 09 Feb 2023
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems Xidong Wu Zhengmian Hu Heng Huang 25 15 0 08 Feb 2023
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 14 9 0 26 Nov 2022
Rieoptax: Riemannian Optimization in JAX Saiteja Utpala Andi Han Pratik Jawanpuria Bamdev Mishra 21 3 0 10 Oct 2022
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints Lei Wang Xin Liu 6 8 0 29 Aug 2022
Riemannian accelerated gradient methods via extrapolation Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 16 7 0 13 Aug 2022
Riemannian Stochastic Gradient Method for Nested Composition Optimization Dewei Zhang S. Tajbakhsh 17 1 0 19 Jul 2022
Riemannian Natural Gradient Methods Jiang Hu Ruicheng Ao Anthony Man-Cho So Minghan Yang Zaiwen Wen 29 10 0 15 Jul 2022
Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models Ainkaran Santhirasekaram Avinash Kori A. Rockall Mathias Winkler Francesca Toni Ben Glocker FAtt 42 4 0 05 Jul 2022
On a class of geodesically convex optimization problems solved via Euclidean MM methods Melanie Weber S. Sra 9 4 0 22 Jun 2022
Anchor Sampling for Federated Learning with Partial Client Participation Feijie Wu Song Guo Zhihao Qu Shiqi He Ziming Liu Jing Gao FedML 28 12 0 13 Jun 2022
Federated Learning on Riemannian Manifolds Jiaxiang Li Shiqian Ma FedML 13 13 0 12 Jun 2022
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces Michael I. Jordan Tianyi Lin Emmanouil-Vasileios Vlatakis-Gkaragkounis 29 19 0 04 Jun 2022
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport Lingkai Kong Yuqing Wang Molei Tao ODL 33 9 0 27 May 2022
Differentially private Riemannian optimization Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 38 10 0 19 May 2022
Riemannian Hamiltonian methods for min-max optimization on manifolds Andi Han Bamdev Mishra Pratik Jawanpuria Pawan Kumar Junbin Gao 35 17 0 25 Apr 2022
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing Jun Qi Chao-Han Huck Yang Pin-Yu Chen Javier Tejedor 25 16 0 11 Mar 2022
The Flag Median and FlagIRLS Nathan Mankovich E. King C. Peterson Michael Kirby 38 9 0 08 Mar 2022
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm Peiyuan Zhang J.N. Zhang S. Sra 21 8 0 13 Feb 2022
Trivial bundle embeddings for learning graph representations Zheng Xie Xiaojing Zuo Yiping Song 18 0 0 05 Dec 2021
Understanding Riemannian Acceleration via a Proximal Extragradient Framework Jikai Jin S. Sra 19 6 0 04 Nov 2021
Distributed Principal Component Analysis with Limited Communication Foivos Alimisis Peter Davies Bart Vandereycken Dan Alistarh 32 12 0 27 Oct 2021
Projective Manifold Gradient Layer for Deep Rotation Regression Jiayi Chen Yingda Yin Tolga Birdal Baoquan Chen Leonidas J. Guibas He Wang 47 33 0 22 Oct 2021