Reinforcement Learning on Non-Euclidean Spaces: Swarms, Spheres, and Hyperbolic RL
4 Feb 2026
Learn about stochastic policies using Bingham, spherical Cauchy, and hyperbolic latent representations.
Supervised Learning for Swarms on Manifolds: Training Kuramoto Networks and Stochastic Optimization
4 Feb 2026
Explore Maximum Likelihood, Score Matching, and Evolutionary Optimization (CMA ES) on manifolds.
Swarms on Manifolds for Deep Learning: Training Kuramoto Models and Trajectory Learning
4 Feb 2026
Discover parameter estimation for wrapped Cauchy and von Mises distributions in trajectory learning.
Probabilistic ML on Grassmannians and Orthogonal Groups: Langevin and Bingham Matrix Models
4 Feb 2026
Master statistical ML on Grassmannians and orthogonal groups. Learn how matrix Bingham and matrix Langevin distributions derive from vMF spherical models.
Hyperbolic Space Statistical Models: Geometric Deep Learning & Inference
4 Feb 2026
Discover how statistical models over hyperbolic spaces enable inference, sampling, and density estimation in Geometric Deep Learning
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.