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Found 5 papers, 4 papers with code
Riemannian Laplace Approximation with the Fisher Metric
Laplace's method approximates a target density with a Gaussian distribution at its mode.
Warped geometric information on the optimisation of Euclidean functions
We use Riemannian geometry notions to redefine the optimisation problem of a function on the Euclidean space to a Riemannian manifold with a warped metric, and then find the function's optimum along this manifold.
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks.
Lagrangian Manifold Monte Carlo on Monge Patches
The efficiency of Markov Chain Monte Carlo (MCMC) depends on how the underlying geometry of the problem is taken into account.
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters that are typically selected by Bayesian optimization or cross-validation.
