most citedIs In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

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stat.ML2026

FairBED: Incorporating Fairness into Bayesian Experimental Design

Marcel Hedman, Emily Alger, Brieuc Lehmann +2

Bayesian experimental design (BED) has traditionally focused exclusively on collecting data that is maximally informative, with little consideration of other desirable dataset char…

stat.ML2024★ 2 cited

Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

Fabian Falck, Ziyu Wang, Chris Holmes

In-context learning (ICL) has emerged as a particularly remarkable characteristic of Large Language Models (LLM): given a pretrained LLM and an observed dataset, LLMs can make pred…

stat.ML2024

On Uncertainty Quantification for Near-Bayes Optimal Algorithms

Ziyu Wang, Chris Holmes

Bayesian modelling allows for the quantification of predictive uncertainty which is crucial in safety-critical applications. Yet for many machine learning (ML) algorithms, it is di…