EXONEST: The Bayesian Exoplanetary Explorer. - Kevin H. Knuth; Ben Placek; Daniel Angerhausen; Jennifer L. Carter; Bryan D'Angelo; Anthony D. Gai; Bertrand Carado (Entropy, 2017)

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval. - Frank Soboczenski; Michael D. Himes; Molly D. O'Beirne; Simone Zorzan; Atilim Gunes Baydin; Adam D. Cobb; Daniel Angerhausen; Giada N. Arney; Shawn D. Domagal-Goldman (CoRR, 2018)

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. - Adam D. Cobb; Michael D. Himes; Frank Soboczenski; Simone Zorzan; Molly D. O'Beirne; Atilim Günes Baydin; Yarin Gal; Shawn D. Domagal-Goldman; Giada N. Arney; Daniel Angerhausen (CoRR, 2019)

Unsupervised Distribution Learning for Lunar Surface Anomaly Detection. - Adam Lesnikowski; Valentin Tertius Bickel; Daniel Angerhausen (CoRR, 2020)

Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks. - Timothy D. Gebhard; Daniel Angerhausen; Björn S. Konrad; Eleonora Alei; Sascha P. Quanz; Bernhard Schölkopf (CoRR, 2023)

Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling. - Timothy D. Gebhard; Jonas Wildberger; Maximilian Dax; Daniel Angerhausen; Sascha P. Quanz; Bernhard Schölkopf (CoRR, 2023)

Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels. - Timothy D. Gebhard; Jonas Wildberger; Maximilian Dax; Annalena Kofler; Daniel Angerhausen; Sascha P. Quanz; Bernhard Schölkopf (CoRR, 2024)

Automated Discovery of Anomalous Features in Ultralarge Planetary Remote-Sensing Datasets Using Variational Autoencoders. - Adam Lesnikowski; Valentin Tertius Bickel; Daniel Angerhausen (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)