activity
20242026
collaborators

8 papers

stat.ML2026

Anchor PCA

Benedikt Seiter, Anya Fries, Julius von Kügelgen +1

Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal…

cs.LG2026

FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes

Anya Fries, Jacob A Nelson, Martin Jung +2

We introduce FLUXtrapolation, a benchmark for extrapolating ecosystem fluxes under progressively harder distribution shifts. Ecosystem fluxes are central to understanding the carbo…

stat.ML2026

Prediction-Intervention Games and Invariant Sets

Linus Kühne, Felix Schur, Jonas Peters

We consider the following two-player game: using observational data, the leader chooses a prediction function for a response variable from given covariates. The follower then r…

stat.ML2026

Worst-case low-rank approximations

Anya Fries, Markus Reichstein, David Blei +1

Real-world data in health, economics, and environmental sciences are often collected across heterogeneous domains (such as hospitals, regions, or time periods). In such settings, d…

stat.ML2026

Maximum Risk Minimization with Random Forests

Francesco Freni, Anya Fries, Linus Kühne +2

We consider a regression setting where observations are collected in different environments modeled by different data distributions. The field of out-of-distribution (OOD) generali…

stat.ML2026

Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?

Felix Schur, Niklas Pfister, Peng Ding +2

We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates and an outcome under different…