5 papers
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…
Sanity Checking Causal Representation Learning on a Simple Real-World System
Juan L. Gamella, Simon Bing, Jakob Runge
We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical ex…
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
Juan L. Gamella, Jonas Peters, Peter Bühlmann
In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers m…