6 citations · 11 across the 3 of their papers we have counts for
9 papers
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters +1
We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are ava…
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
Sebastian Weichwald, Jonas Peters
While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict, for example, how cognitive functions are a…
Causal models for dynamical systems
Jonas Peters, Stefan Bauer, Niklas Pfister
A probabilistic model describes a system in its observational state. In many situations, however, we are interested in the system's response under interventions. The class of struc…
Causal discovery in heavy-tailed models
Nicola Gnecco, Nicolai Meinshausen, Jonas Peters +1
Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are…
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…