Probabilistic Learning on Spheres: von Mises-Fisher, Spherical Cauchy, and Bingham Distributions
3 Feb 2026
Explore statistical models for spheres in Machine Learning. Learn about vMF, Bingham, and Poisson kernel distributions for unsupervised learning and RL.
Statistical Models on Circles and Tori: von Mises, Wrapped Cauchy, and Kato-Jones Distributions
30 Jan 2026
Explore probabilistic modeling on torical manifolds using von Mises, Wrapped Cauchy, and Kato-Jones distributions linked to Kuramoto models
Directional Statistics and Swarming Dynamics for Riemannian Manifold ML
30 Jan 2026
Learn why Gaussian models fail on curved spaces and how Kuramoto models offer a robust alternative.
Consensus Algorithms on Manifolds: Stiefel, Siegel, and Kuramoto Dynamics
28 Jan 2026
Explore consensus algorithms on Stiefel manifolds and Siegel domains. Learn how Kuramoto models act as continuous-time algorithms to minimize disagreement.
Kuramoto Models as Gradient Flows: Langevin Dynamics and Hyperbolic Geometry
28 Jan 2026
Explore Kuramoto models as gradient flows across spheres, unitary groups, and hyperbolic balls.
Generalizing Kuramoto Models: Collective Motion on Lie Groups and Spheres in Machine Learning
28 Jan 2026
Learn about complex-valued ODEs, Riccati equations, and non-Euclidean dynamics for advanced Geometric Deep Learning.
Generalized Kuramoto Models: Dynamics on Manifolds, Lie Groups, and Spheres
27 Jan 2026
Learn about complex-valued ODEs, geometric Riccati equations, and collective particle motion on Lie groups and higher-dimensional spheres.
Solving Non-Convex HSVMs via Semidefinite Relaxation (SDP)
27 Jan 2026
Learn how Hyperbolic SVMs utilize Minkowski products and SDP relaxations, and how the Kuramoto model generalizes phase synchronization
Kuramoto Models on Higher-Dimensional Manifolds: Geometric Riccati Matrix ODEs
27 Jan 2026
Learn how collective motion and interacting particles are modeled using geometric Riccati matrix ODEs.