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Found 5 papers, 1 papers with code
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control
RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
Statistical inverse learning problems with random observations
To achieve these results, the structure of reproducing kernel Hilbert spaces is leveraged to establish minimax rates in the statistical learning setting.
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics
In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention.
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often computationally intractable due to the large computational cost of evaluating the data likelihood.
Convex regularization in statistical inverse learning problems
We consider a statistical inverse learning problem, where the task is to estimate a function $f$ based on noisy point evaluations of $Af$, where $A$ is a linear operator.
