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Bayesian Deep Learning is a field that combines Bayesian probability theory with deep learning. It aims to provide a principled way of quantifying uncertainty in neural network predictions.
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 BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data Marta S. Lemanczyk Lucas Kock Johanna Schlimme Nadja Klein Bernhard Y. Renard | |||
 DeepBlip: Estimating Conditional Average Treatment Effects Over Time Haorui Ma Dennis Frauen Stefan Feuerriegel | |||
 Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts Xinlei Xiong Wenbo Hu Shuxun Zhou Kaifeng Bi Lingxi Xie Ying Liu Richang Hong Qi Tian | |||
 Notes on Kernel Methods in Machine Learning Diego Armando Pérez-Rosero Danna Valentina Salazar-Dubois Juan Camilo Lugo-Rojas Andrés Marino Álvarez-Meza Germán Castellanos-Dominguez | |||
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 Neural Variational Dropout ProcessesInternational Conference on Learning Representations (ICLR), 2025 | |||
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