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Alexander Immer

Hodge-Aware Contrastive Learning
Improving Neural Additive Models with Bayesian Principles
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Invariance learning in deep neural networks with differentiable Laplace approximations
Pathologies in Priors and Inference for Bayesian Transformers
Probing as quantifying inductive bias
Scalable marginal likelihood estimation for model selection in deep learning

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