Integration against an intractable probability measure is among the fundamental challenges of Bayesian inference. A useful approach to this problem seeks a deterministic coupling of the measure of interest with a tractable “reference” measure (e.g., a standard Gaussian). This coupling is induced by a transport map, and enables direct simulation …
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. We overview some recent work [Xu and Raginsky (2017)] that bounds generalization error of empirical risk minimization based on the mutual information I(S;W) between the algorithm …
Willem’s Abstract: Atmospheric light detection and ranging (lidar) provides a unique capability to resolve atmospheric vertical structures of clouds and aerosols with very high sensitivity at high altitude and temporal resolutions. A variety of lidar instruments exist which focus on the measurement of the optical properties of the atmosphere in …
With the availability of huge amounts of unlabeled data, unsupervised learning methods are gaining increasing popularity and importance. We focus on ”unsupervised ensemble learning”, where one obtains the predictions of multiple classifiers over a set of unlabeled instances. The classifiers may be human experts as in crowdsourcing, or prediction algorithms …