Detecting anthropogenic cloud perturbations with deep learning. - Duncan Watson-Parris; Samuel Sutherland; Matthew Christensen; Anthony L. Caterini; Dino Sejdinovic; Philip Stier (CoRR, 2019)

Emulating Aerosol Microphysics with Machine Learning. - Paula Harder; Duncan Watson-Parris; Dominik Strassel; Nicolas R. Gauger; Philip Stier; Janis Keuper (CoRR, 2021)

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. - Andrew Jesson; Peter Manshausen; Alyson Douglas; Duncan Watson-Parris; Yarin Gal; Philip Stier (CoRR, 2021)

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Maëlys Solal; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (NeurIPS, 2022)

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (CoRR, 2022)

Physics-Informed Learning of Aerosol Microphysics. - Paula Harder; Duncan Watson-Parris; Philip Stier; Dominik Strassel; Nicolas R. Gauger; Janis Keuper (CoRR, 2022)

Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval. - Paolo Pelucchi; Jorge Vicent Servera; Philip Stier; Gustau Camps-Valls (IEEE Trans. Geosci. Remote. Sens., 2025)

Lossy Neural Compression for Geospatial Analytics: A Review. - Carlos Gomes; Isabelle Wittmann; Damien Robert 002; Johannes Jakubik; Tim Reichelt; Michele Martone; Stefano Maurogiovanni; Rikard Vinge; Jonas Hurst; Erik Scheurer; Rocco Sedona; Thomas Brunschwiler; et al. (CoRR, 2025)