76 citations · 234 across the 28 of their papers we have counts for
3 papers · 2 filters
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Raphael Suter, Đorđe Miladinović, Bernhard Schölkopf +1
The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use o…
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Niklas Pfister, Stefan Bauer, Jonas Peters
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference…
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer +3
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data ha…