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Modelling Pathwise Uncertainty of Stochastic Differential Equations Samplers via Probabilistic Numerics
Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past decade as uncertainty-aware numerical integrators. They typically proceed by assuming a functional prior t ...
Bayesian Anal. Advance Publication 1-24, 2025
Least squares variational inference
Variational inference seeks the best approximation of a target distribution within a chosen family, where "best" means minimizing Kullback-Leibler divergence. When the approximation family is exponent ...
The 39th Annual Conference on Neural Information Processing Systems, 2025