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Showing papers from LMU Munich, MCML Show all papers

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NeurIPS 2026Ludwig-Maximilians-Universität MKings College LondonLMU Munich, MCMLLMUKing's College LondonLLM evaluation & benchmarks

S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs

Yawei Li, Jiazheng Li, David Rügamer, Bernd Bischl and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026OralLMU Munich, MCMLMohamed bin Zayed University of Technology Innovation InstituteMarburguniversityBayesian & probabilistic methods

The Aleatoric-Epistemic Dichotomy of Uncertainty is Meaningful and Indispensable for Machine Learning

Yusuf Sale, Nikita Kotelevskii, Maxim Panov, Eyke Hüllermeier

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026HTWG Konstanz - University of ApLMU Munich, MCMLLMU Munich | Htwg KonstanzInstruction tuning

On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference

LoRA-Curve constructs continuous low-loss Bézier valleys between independent LoRA optima, improving Bayesian model averaging and predictive mutual information without sacrificing accuracy.

Daniel Dold, Emanuel Sommer, Julius Kobialka, Oliver Dürr and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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