A placeholder for learning on curved spaces. Not discussed: learning OF the curvature of spaces.

AFAICT this usually boils down to defining an appropriate stochastic process on a manifold.

## Learning on a given manifold

Learning where there is an *a priori* manifold seems to also be a usage here? For example the manifold of positive definite matrices is treated in depth in Chikuse and 筑瀬 (2003).

See the work of, e.g.

Manifold optimisation implementations:

- pytorch: Lezcano/geotorch: Constrained optimization toolkit for PyTorch (Lezcano Casado 2019)
- MATLAB: manopt,
- Python: pymanopt.
- Julia: Manopt.jl
- Python: Nina Miolane et al’s Geomstats project.
- C++: ROPTLIB (Huang et al. 2018)
- R: ManifoldOptim wrapes ROPTLIB (Martin et al. 2016)

There are at least two textbooks online:

## Information Geometry

The unholy offspring of Fisher information and differential geometry, about which I know little except that it sounds like it should be intuitive. It is probably synonymous with some of the other items on this page if I could sort out all this terminology. See information geometry.

## Hamiltonian Monte Carlo

You can also discuss Hamiltonian Monte Carlo in this setting. I will not.

## Langevin Monte Carlo

Girolami et al discuss Langevin Monte Carlo in this context.

## Natural gradient

See natural gradients.

## Homogeneous probability

Albert Tarantola’s framing, from his manuscript. How does it relate to information geometry? I don’t know yet. Haven’t had time to read. Also not a common phrasing, which is a danger sign.

## Incoming

- Agustinus Kristiadi, Fisher Information Matrix
- Agustinus Kristiadi, Hessian and Curvatures in Machine Learning: A Differential-Geometric View
- Agustinus Kristiadi, Notes on Riemannian Geometry
- Agustinus Kristiadi, Optimization and Gradient Descent on Riemannian Manifolds

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