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cs.LG2026
Decision-Aligned Evaluation of Uncertainty Quantification
Annika Schneider, Tommy Rochussen, Joshua Stiller +1
Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on…
cs.LG2026
In-Context Function Learning in Large Language Models
Elif Akata, Konstantinos Voudouris, Vincent Fortuin +1
Large language models (LLMs) can learn from a few demonstrations provided at inference time. We study this in-context learning phenomenon through the lens of Gaussian Processes (GP…