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David Brellmann

2 accepted papers

2025

Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the Role of Model Complexity

NeurIPS 2025poster

**Out-of-distribution (OOD) detection** is essential for ensuring the reliability and safety of machine learning systems. In recent years, it has received increasing attention, particularly through post-hoc detection and training-based methods. In this paper, we focus on **post-hoc OOD detection**,…

Cited by 0SourceScholar
2024

On Double Descent in Reinforcement Learning with LSTD and Random Features

ICLR 2024poster

Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervised learning, the regime of over-parameterization and its benefits are well understood, the situation in RL is much less…

Cited by 2SourcePDFScholar