arXiv Machine Learning By Rowan Iskandar

Value of Information under Imprecise Probabilities: Decision-Rule-Specific Values and Fixed-Measure Envelopes on a Credal Set

Read the original on arXiv Machine Learning →

arXiv:2607. 06570v1 Announce Type: cross Abstract: Value-of-information (VOI) analysis is usually conducted under a single probability measure.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 4

Tail-Aware Information-Theoretic Bounds for LLM Alignment under Heavy-Tailed Rewards

arXiv:2604. 10727v2 Announce Type: replace-cross Abstract: Classical information-theoretic learning bounds typically rely on KL mutual information and moment-generating-function (MGF) arguments, which are well matched to bounded or sub-Gaussian losses but can be ineffective when losses or rewards are heavy-tailed.

By Huiming Zhang, Binghan Li, Wan Tian, Qiang Sun