4 papers
Convergence of a class of gradient-free optimisation schemes when the objective function is noisy, irregular, or both
Christophe Andrieu, Nicolas Chopin, Ettore Fincato +1
We investigate the convergence properties of a class of iterative algorithms designed to minimize a potentially non-smooth and noisy objective function, which may be algebraically…
Gradient-free optimization via integration
Christophe Andrieu, Nicolas Chopin, Ettore Fincato +1
We develop and analyse an approach to optimize functions not assumed to be convex, differentiable or even continuous. The algorithm be…
Self-Organizing State-Space Models with Artificial Dynamics
Yuan Chen, Mathieu Gerber, Christophe Andrieu +1
We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter . A popular idea to address this problem con…
Adaptive stratified Monte Carlo using decision trees
Nicolas Chopin, Hejin Wang, Mathieu Gerber
It has been known for a long time that stratification is one possible strategy to obtain higher convergence rates for the Monte Carlo estimation of integrals over the hyper-cube $[…