Sequential learning for decision support under uncertainty

Abstract: In many applicational areas there is a need to determine a control variable that optimizes a pre-specified objective. This problem is particularly challenging when knowledge on the underlying dynamics is subject to various sources of uncertainty. A scenario such as that arises for instance in the context of therapy individualization to improve the efficacy and safety of medical treatment. Mathematical models describing the pharmacokinetics and pharmacodynamics of a drug together with data on associated biomarkers can be leveraged to support decision-making by predicting therapy outcomes. We present a continuous learning strategy which follows a novel sequential Monte Carlo tree search approach and explore how the underlying uncertainties reflect in the approximated control variable.

analysis of PDEsfunctional analysisgeneral mathematicsnumerical analysisoptimization and controlprobabilitystatistics theory

Audience: researchers in the topic

One World seminar: Mathematical Methods for Arbitrary Data Sources (MADS)

Series comments: Description: Research seminar on mathematics for data

The lecture series will collect talks on mathematical disciplines related to all kind of data, ranging from statistics and machine learning to model-based approaches and inverse problems. Each pair of talks will address a specific direction, e.g., a NoMADS session related to nonlocal approaches or a DeepMADS session related to deep learning.

Approximately 15 minutes prior to the beginning of the lecture, a zoom link will be provided on the official website and via mailing list. For further details please visit our webpage.