Author

G. Kaissis

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Aug 2026

It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces

This paper proposes a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure, capturing essential output correlations while avoiding the computational burdens of full covariance matrices.

Leonhard F. Feiner, M. Nickel, M. Menten et al. · Trans. Mach. Learn. Res. · 0 citations

Open access Jul 2026

Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming

A Dynamic, Automatic and Systematic red-teaming audit framework that continuously stress-tests LLMs for health across four safety-critical axes: robustness, privacy, bias and hallucination, which provides a scalable framework for surfacing latent risks before such systems are deployed in consumer-facing health assistants and broader clinical workflows.

Jiazhen Pan, Bailiang Jian, Paul Hager et al. · Nature Health · 0 citations

Preprint Aug 2026

Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching

Continuous depth batching (CDB) is introduced, which schedules at the granularity of individual loop iterations, and handles boundary stages and loop steps in separate priority queues, makes exit decisions one step ahead, and overlaps all scheduling work with GPU computation.

Kristian Schwethelm, D. Rueckert, G. Kaissis · 0 citations