Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

10 March 2016

Papers citing "Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation"

50 / 51 papers shown

Title
Lie Group Symmetry Discovery and Enforcement Using Vector Fields Ben Shaw Sasidhar Kunapuli Abram Magner Kevin R. Moon 37 0 0 13 May 2025
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces Anthony Nouy Alexandre Pasco 47 0 0 03 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 50 0 0 26 Apr 2025
Riemannian Optimization on Relaxed Indicator Matrix Manifold Jinghui Yuan Fangyuan Xie Feiping Nie Xuelong Li 77 0 0 26 Mar 2025
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices Thibault de Surrel Fabien Lotte Sylvain Chevallier Florian Yger 67 0 0 03 Feb 2025
Manifold learning and optimization using tangent space proxies Ryan A. Robinett Lorenzo Orecchia Samantha J. Riesenfeld 43 0 0 22 Jan 2025
Residual Deep Gaussian Processes on Manifolds Kacper Wyrwal Andreas Krause Viacheslav Borovitskiy BDL 57 0 0 31 Oct 2024
A Metric-based Principal Curve Approach for Learning One-dimensional Manifold E. Cui 18 0 0 20 May 2024
Cluster Exploration using Informative Manifold Projections Stavros Gerolymatos Xenophon Evangelopoulos V. Gusev John Y. Goulermas 19 0 0 26 Sep 2023
A survey of manifold learning and its applications for multimedia Hannes Fassold 47 1 0 08 Sep 2023
Towards frugal unsupervised detection of subtle abnormalities in medical imaging Geoffroy Oudoumanessah Carole Lartizien M. Dojat Florence Forbes MedIm 25 2 0 04 Sep 2023
Gradient-Based Spectral Embeddings of Random Dot Product Graphs Marcelo Fiori Bernardo Marenco Federico Larroca P. Bermolen Gonzalo Mateos BDL 29 3 0 25 Jul 2023
Medoid splits for efficient random forests in metric spaces Matthieu Bulté Helle Sorensen 16 4 0 29 Jun 2023
Alignment of Density Maps in Wasserstein Distance A. Singer Ruiyi Yang 33 8 0 21 May 2023
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies Hanna Ziesche Leonel Rozo 26 5 0 17 May 2023
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization Yian Deng Tingting Mu 28 1 0 22 Feb 2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold Yanhong Fei Xian Wei Yingjie Liu Zhengyu Li Mingsong Chen 33 6 0 16 Feb 2023
Riemannian Optimization for Variance Estimation in Linear Mixed Models L. Sembach J. P. Burgard Volker Schulz 9 0 0 18 Dec 2022
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis Dong Min Roh Z. Bai Ren-Cang Li 18 1 0 21 Nov 2022
Finding the global semantic representation in GAN through Frechet Mean Jaewoong Choi Geonho Hwang Hyunsoo Cho Myung-joo Kang GAN DRL 32 2 0 11 Oct 2022
Stochastic Neuromorphic Circuits for Solving MAXCUT Bradley H. Theilman Yipu Wang Ojas D. Parekh William M. Severa John Smith J. Aimone 21 7 0 05 Oct 2022
Manifold Free Riemannian Optimization B. Shustin H. Avron B. Sober 36 2 0 07 Sep 2022
Human-to-Robot Manipulability Domain Adaptation with Parallel Transport and Manifold-Aware ICP Anna Reithmeir Luis F. C. Figueredo Sami Haddadin 26 5 0 16 Aug 2022
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG Reinmar J. Kobler J. Hirayama Qibin Zhao M. Kawanabe 19 54 0 02 Jun 2022
Riemannian Hamiltonian methods for min-max optimization on manifolds Andi Han Bamdev Mishra Pratik Jawanpuria Pawan Kumar Junbin Gao 38 17 0 25 Apr 2022
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design Xiran Fan Chun-Hao Yang B. Vemuri 42 18 0 03 Dec 2021
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks Faria Huq Adrish Dey Sahra Yusuf Dena Bazazian Tolga Birdal Nina Miolane 51 1 0 29 Nov 2021
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning Buyun Liang Tim Mitchell Ju Sun 17 3 0 27 Nov 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels Noémie Jaquier Viacheslav Borovitskiy A. Smolensky Alexander Terenin Tamim Asfour Leonel Rozo 34 35 0 02 Nov 2021
Topologically Regularized Data Embeddings R. Vandaele Bo Kang Jefrey Lijffijt T. D. Bie Yvan Saeys 21 9 0 18 Oct 2021
Simulation-based Bayesian inference for multi-fingered robotic grasping Norman Marlier O. Bruls Gilles Louppe 37 6 0 29 Sep 2021
Nonlinear matrix recovery using optimization on the Grassmann manifold Florentin Goyens C. Cartis Armin Eftekhari 29 6 0 13 Sep 2021
Interactive Dimensionality Reduction for Comparative Analysis Takanori Fujiwara Xinhai Wei Jian Zhao K. Ma 27 30 0 29 Jun 2021
Faster Randomized Methods for Orthogonality Constrained Problems B. Shustin H. Avron 24 2 0 22 Jun 2021
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 29 38 0 01 Jun 2021
The Complex-Step Derivative Approximation on Matrix Lie Groups C. C. Cossette A. Walsh James Richard Forbes 27 18 0 06 May 2021
A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models L. Sembach J. P. Burgard Volker Schulz 24 3 0 30 Apr 2021
Manifold optimization for non-linear optimal transport problems Bamdev Mishra N. Satyadev Hiroyuki Kasai Pratik Jawanpuria OT 14 10 0 01 Mar 2021
Bayesian Quadrature on Riemannian Data Manifolds Christian Frohlich A. Gessner Philipp Hennig Bernhard Schölkopf Georgios Arvanitidis 29 4 0 12 Feb 2021
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics Du Nguyen 16 5 0 21 Sep 2020
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction Adam Farooq Yordan P. Raykov P. Raykov Max A. Little 13 0 0 22 Jun 2020
Learning Geometric Word Meta-Embeddings Pratik Jawanpuria N. Dev Anoop Kunchukuttan Bamdev Mishra FedML 28 13 0 20 Apr 2020
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning Noémie Jaquier Leonel Rozo Sylvain Calinon Mathias Bürger 17 53 0 11 Oct 2019
Coresets for Gaussian Mixture Models of Any Shape Dan Feldman Zahi Kfir Xuan Wu 18 11 0 12 Jun 2019
McTorch, a manifold optimization library for deep learning Mayank Meghwanshi Pratik Jawanpuria Anoop Kunchukuttan Hiroyuki Kasai Bamdev Mishra AI4CE 28 41 0 03 Oct 2018
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach Pratik Jawanpuria Arjun Balgovind Anoop Kunchukuttan Bamdev Mishra 38 77 0 27 Aug 2018
geomstats: a Python Package for Riemannian Geometry in Machine Learning Nina Miolane Johan Mathe Claire Donnat Mikael Jorda Xavier Pennec AI4CE 37 123 0 21 May 2018
Optimal projection of observations in a Bayesian setting L. Giraldi O. Maître Ibrahim Hoteit Omar Knio 32 8 0 19 Sep 2017
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization Sean Martin Andrew M. Raim Wen Huang K. Adragni 21 13 0 12 Dec 2016
Wasserstein Discriminant Analysis Rémi Flamary Marco Cuturi Nicolas Courty A. Rakotomamonjy 23 100 0 29 Aug 2016