Riemannian optimization

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Most implemented papers archive 2025-07-28

30 shown of 50 papers with code (153 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Poincaré Embeddings for Learning Hierarchical Representations
  22 May 2017 9 repositories listed Syntology ran 6 of 14 samples · 8 unverified · 6 pointer-only (licence)
  Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
• Exponential Machines
  12 May 2016 3 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  Modeling interactions between features improves the performance of machine learning solutions in many domains (e.
• A Spectral Framework for Tracking Communities in Evolving Networks
  10 Dec 2024 2 repositories listed
  Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology.
• Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
  9 Jun 2021 2 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets.
• Geoopt: Riemannian Optimization in PyTorch
  6 May 2020 2 repositories listed
  Geoopt is a research-oriented modular open-source package for Riemannian Optimization in PyTorch.
• geomstats: a Python Package for Riemannian Geometry in Machine Learning
  21 May 2018 2 repositories listed
  This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry.
• Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
  18 May 2025 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  Extensive experiments verify the benefits of the proposed method, across a wide variety of problems.
• Riemannian Optimization on Relaxed Indicator Matrix Manifold
  26 Mar 2025 1 repository listed
  The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem.
• Causal Abstraction Learning based on the Semantic Embedding Principle
  1 Feb 2025 1 repository listed
  We present a category-theoretic approach to SCMs that enables the learning of a CA by finding a morphism between the low- and high-level probability measures, adhering to the semantic embedding principle.
• Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning
  5 Nov 2024 1 repository listed
  This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning.
• The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations
  19 Sep 2024 1 repository listed
  Visual counterfactual explanation (CF) methods modify image concepts, e.
• Optimal Matrix-Mimetic Tensor Algebras via Variable Projection
  11 Jun 2024 1 repository listed
  The choice of linear mapping is crucial to representation quality and, in practice, is made heuristically based on expected correlations in the data.
• Optimization without Retraction on the Random Generalized Stiefel Manifold
  2 May 2024 1 repository listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  Optimization over the set of matrices X that satisfy X^⊤ B X = Iₚ, referred to as the generalized Stiefel manifold, appears in many applications involving sampled covariance matrices such as the canonical correlation…
• FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching
  1 Apr 2024 1 repository listed Syntology ran 9 of 9 samples · 0 unverified
  The paper proposes FireANTs, the first multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching.
• Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation
  27 Mar 2024 1 repository listed
  Finally, we propose and analyze an estimator of the inverse covariance matrix of random regression coefficients based on data from the training tasks.
• Registration of algebraic varieties using Riemannian optimization
  16 Jan 2024 1 repository listed
  Our approach is particularly useful when the two point clouds describe different parts of an objects (which may not even be overlapping), on the condition that the surface of the object may be well approximated by a set…
• Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
  11 Jan 2024 1 repository listed
  The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
• Fast hyperboloid decision tree algorithms
  20 Oct 2023 1 repository listed
  In response to these challenges, we present hyperDT, a novel extension of decision tree algorithms into hyperbolic space.
• Low-complexity subspace-descent over symmetric positive definite manifold
  3 May 2023 1 repository listed
  This work puts forth low-complexity Riemannian subspace descent algorithms for the minimization of functions over the symmetric positive definite (SPD) manifold.
• Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
  29 Mar 2023 1 repository listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  Orthogonality constraints naturally appear in many machine learning problems, from principal component analysis to robust neural network training.
• A Riemannian ADMM
  3 Nov 2022 1 repository listed
  Our algorithm is the first ADMM type algorithm that minimizes a nonsmooth objective over manifold -- a particular nonconvex set.
• Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
  25 Oct 2022 1 repository listed
  The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG).
• Learning Graphical Factor Models with Riemannian Optimization
  21 Oct 2022 1 repository listed
  Graphical models and factor analysis are well-established tools in multivariate statistics.
• Rieoptax: Riemannian Optimization in JAX
  10 Oct 2022 1 repository listed
  We present Rieoptax, an open source Python library for Riemannian optimization in JAX.
• Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
  19 Sep 2022 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called "neural collapse" phenomenon.
• Riemannian optimization for non-centered mixture of scaled Gaussian distributions
  7 Sep 2022 1 repository listed
  The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem.
• Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
  17 Jun 2022 1 repository listed
  We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without the prior knowledge of its intrinsic rank.
• Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
  12 May 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  In particular, inspired by a numerical integration method on manifolds called Feedback Integrators, we propose to instantiate it on the tangent bundle of the Stiefel manifold for the first time.
• Riemannian block SPD coupling manifold and its application to optimal transport
  30 Jan 2022 1 repository listed
  In this work, we study the optimal transport (OT) problem between symmetric positive definite (SPD) matrix-valued measures.
• Projective Manifold Gradient Layer for Deep Rotation Regression
  22 Oct 2021 1 repository listed
  Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.