28 citations · 59 across the 8 of their papers we have counts for
8 papers
Counterfactual-based Root Cause Analysis for Dynamical Systems
Juliane Weilbach, Sebastian Gerwinn, Karim Barsim +1
Identifying the underlying reason for a failing dynamic process or otherwise anomalous observation is a fundamental challenge, yet has numerous industrial applications. Identifying…
Estimation of Counterfactual Interventions under Uncertainties
Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir +1
Counterfactual analysis is intuitively performed by humans on a daily basis eg. "What should I have done differently to get the loan approved?". Such counterfactual questions also…
Validation of Composite Systems by Discrepancy Propagation
David Reeb, Kanil Patel, Karim Barsim +2
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-…
Inferring the Structure of Ordinary Differential Equations
Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach +1
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements. While accurate prediction can be achieved w…
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann, Sebastian Gerwinn, Andreas Look +2
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity…
Bayesian Evidential Deep Learning with PAC Regularization
Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has…
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
David Reeb, Andreas Doerr, Sebastian Gerwinn +1
Gaussian Processes (GPs) are a generic modelling tool for supervised learning. While they have been successfully applied on large datasets, their use in safety-critical application…
In All Likelihood, Deep Belief Is Not Enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz +1
Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively…