BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. - Dominik Rothenhäusler; Christina Heinze; Jonas Peters; Nicolai Meinshausen (NIPS, 2015)

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. - Christina Heinze; Brian McWilliams; Nicolai Meinshausen (AISTATS, 2016)

Preserving Differential Privacy Between Features in Distributed Estimation. - Christina Heinze-Deml; Brian McWilliams; Nicolai Meinshausen (CoRR, 2017)

Guarding Against Adversarial Domain Shifts with Counterfactual Regularization. - Christina Heinze-Deml; Nicolai Meinshausen (CoRR, 2017)

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness. - Fanny Yang; Zuowen Wang; Christina Heinze-Deml (NeurIPS, 2019)

Active Invariant Causal Prediction: Experiment Selection through Stability. - Juan Gamella; Christina Heinze-Deml (NeurIPS, 2020)

Think before you act: A simple baseline for compositional generalization. - Christina Heinze-Deml; Diane Bouchacourt (CoRR, 2020)

Conditional variance penalties and domain shift robustness. - Christina Heinze-Deml; Nicolai Meinshausen (Mach. Learn., 2021)

Do LLMs "know" internally when they follow instructions? - Juyeon Heo; Christina Heinze-Deml; Oussama Elachqar; Shirley You Ren; Udhay Nallasamy; Andy Miller; Kwan Ho Ryan Chan; Jaya Narain (CoRR, 2024)

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. - Arno Blaas; Adam Golinski; Andrew C. Miller; Luca Zappella; Jörn-Henrik Jacobsen; Christina Heinze-Deml (CoRR, 2024)

Do LLMs "know" internally when they follow instructions? - Juyeon Heo; Christina Heinze-Deml; Oussama Elachqar; Kwan Ho Ryan Chan; Shirley You Ren; Andrew C. Miller; Udhyakumar Nallasamy; Jaya Narain (ICLR, 2025)

Do LLMs estimate uncertainty well in instruction-following? - Juyeon Heo; Miao Xiong; Christina Heinze-Deml; Jaya Narain (ICLR, 2025)