Averaging Stochastic Gradient Descent on Riemannian Manifolds

26 February 2018

Papers citing "Averaging Stochastic Gradient Descent on Riemannian Manifolds"

50 / 56 papers shown

Title
Online Functional Principal Component Analysis on a Multidimensional Domain Muye Nanshan Nan Zhang Jiguo Cao 26 0 0 04 May 2025
On the asymptotic behaviour of stochastic processes, with applications to supermartingale convergence, Dvoretzky's approximation theorem, and stochastic quasi-Fejér monotonicity Morenikeji Neri Nicholas Pischke Thomas Powell 25 0 0 17 Apr 2025
Riemannian Optimization on Relaxed Indicator Matrix Manifold Jinghui Yuan Fangyuan Xie Feiping Nie Xuelong Li 77 0 0 26 Mar 2025
Riemannian Federated Learning via Averaging Gradient Stream Zhenwei Huang Wen Huang Pratik Jawanpuria Bamdev Mishra FedML 40 1 0 11 Sep 2024
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize Tong Zhang Chris Junchi Li 40 0 0 15 Jul 2024
Riemannian coordinate descent algorithms on matrix manifolds Andi Han Pratik Jawanpuria Bamdev Mishra 53 5 0 04 Jun 2024
Quantitative Convergences of Lie Group Momentum Optimizers Lingkai Kong Molei Tao 38 2 0 30 May 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction Yury Demidovich Grigory Malinovsky Peter Richtárik 61 2 0 11 Mar 2024
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors Yannan He Garvita Tiwari Tolga Birdal J. E. Lenssen Gerard Pons-Moll 34 8 0 05 Mar 2024
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent Benjamin Gess Sebastian Kassing Nimit Rana 45 0 0 02 Feb 2024
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks Andrew W. Rosemberg Mathieu Tanneau Bruno Fanzeres Joaquim Dias Garcia Pascal Van Hentenryck 33 3 0 06 Oct 2023
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds Yong Cai Michael I. Jordan Tianyi Lin Argyris Oikonomou Emmanouil-Vasileios Vlatakis-Gkaragkounis 43 4 0 29 Jun 2023
Low-complexity subspace-descent over symmetric positive definite manifold Yogesh Darmwal K. Rajawat 52 3 0 03 May 2023
Fairness Uncertainty Quantification: How certain are you that the model is fair? Abhishek Roy P. Mohapatra 34 5 0 27 Apr 2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints Pierre Ablin Simon Vary Bin Gao P.-A. Absil 57 8 0 29 Mar 2023
Learning Rate Schedules in the Presence of Distribution Shift Matthew Fahrbach Adel Javanmard Vahab Mirrokni Pratik Worah 29 6 0 27 Mar 2023
Convergence of variational Monte Carlo simulation and scale-invariant pre-training Nilin Abrahamsen Zhiyan Ding Gil Goldshlager Lin Lin DRL 43 2 0 21 Mar 2023
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 35 9 0 26 Nov 2022
Riemannian accelerated gradient methods via extrapolation Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 27 9 0 13 Aug 2022
Riemannian Stochastic Gradient Method for Nested Composition Optimization Dewei Zhang S. Tajbakhsh 31 1 0 19 Jul 2022
On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models P. Esser Frank Nielsen 21 0 0 17 Jun 2022
Riemannian stochastic approximation algorithms Mohammad Reza Karimi Ya-Ping Hsieh P. Mertikopoulos Andreas Krause 29 2 0 14 Jun 2022
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces Michael I. Jordan Tianyi Lin Emmanouil-Vasileios Vlatakis-Gkaragkounis 34 19 0 04 Jun 2022
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm Peiyuan Zhang J.N. Zhang S. Sra 29 8 0 13 Feb 2022
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer Beatrice Acciaio Anastasis Kratsios G. Pammer OOD 54 20 0 31 Jan 2022
Optimal variance-reduced stochastic approximation in Banach spaces Wenlong Mou K. Khamaru Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 44 8 0 21 Jan 2022
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds P. Esser Frank Nielsen 13 2 0 07 Dec 2021
A diffusion-map-based algorithm for gradient computation on manifolds and applications A. A. Gomez A. J. S. Neto J. Zubelli 12 2 0 16 Aug 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance Hongjian Wang Mert Gurbuzbalaban Lingjiong Zhu Umut cSimcsekli Murat A. Erdogdu 26 41 0 20 Feb 2021
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size Alain Durmus P. Jiménez Eric Moulines Salem Said 29 12 0 15 Feb 2021
Fast and accurate optimization on the orthogonal manifold without retraction Pierre Ablin Gabriel Peyré 64 27 0 15 Feb 2021
No-go Theorem for Acceleration in the Hyperbolic Plane Linus Hamilton Ankur Moitra 27 21 0 14 Jan 2021
Global Riemannian Acceleration in Hyperbolic and Spherical Spaces David Martínez-Rubio 50 19 0 07 Dec 2020
Adaptive and Momentum Methods on Manifolds Through Trivializations Mario Lezcano Casado 20 8 0 09 Oct 2020
EigenGame: PCA as a Nash Equilibrium I. Gemp Brian McWilliams Claire Vernade T. Graepel 32 46 0 01 Oct 2020
Curvature-Dependant Global Convergence Rates for Optimization on Manifolds of Bounded Geometry Mario Lezcano-Casado 27 12 0 06 Aug 2020
Momentum Accelerates Evolutionary Dynamics Marc Harper Joshua Safyan 28 2 0 05 Jul 2020
Variance reduction for Riemannian non-convex optimization with batch size adaptation Andi Han Junbin Gao 21 5 0 03 Jul 2020
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu Krishnakumar Balasubramanian S. Volgushev Murat A. Erdogdu 42 50 0 14 Jun 2020
Projection Robust Wasserstein Distance and Riemannian Optimization Tianyi Lin Chenyou Fan Nhat Ho Marco Cuturi Michael I. Jordan 28 68 0 12 Jun 2020
Convergence Analysis of Riemannian Stochastic Approximation Schemes Alain Durmus P. Jiménez Eric Moulines Salem Said Hoi-To Wai 19 10 0 27 May 2020
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization Jiaxiang Li Krishnakumar Balasubramanian Shiqian Ma 12 5 0 25 Mar 2020
Gradient descent algorithms for Bures-Wasserstein barycenters Sinho Chewi Tyler Maunu Philippe Rigollet Austin J. Stromme 25 83 0 06 Jan 2020
Central limit theorems for stochastic gradient descent with averaging for stable manifolds Steffen Dereich Sebastian Kassing 79 11 0 19 Dec 2019
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization Viet Anh Nguyen Soroosh Shafieezadeh-Abadeh Man-Chung Yue Daniel Kuhn W. Wiesemann 19 23 0 17 Oct 2019
Projection-free nonconvex stochastic optimization on Riemannian manifolds Melanie Weber S. Sra 29 17 0 09 Oct 2019
Escaping from saddle points on Riemannian manifolds Yue Sun Nicolas Flammarion Maryam Fazel 31 71 0 18 Jun 2019
Robustness of accelerated first-order algorithms for strongly convex optimization problems Hesameddin Mohammadi Meisam Razaviyayn M. Jovanović 17 41 0 27 May 2019
Some Limit Properties of Markov Chains Induced by Stochastic Recursive Algorithms Abhishek Gupta Hao Chen Jianzong Pi Gaurav Tendolkar 24 0 0 24 Apr 2019
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope Tolga Birdal Umut Simsekli 35 37 0 11 Apr 2019