2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Variational predictive resampling
Laura Battaglia, Stefano Cortinovis, Chris Holmes +2
Bayesian inference provides principled uncertainty quantification, but accurate posterior sampling with MCMC can be computationally prohibitive for modern applications. Variational…
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…
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
Ziyu Wang, Chris Holmes
Applications of large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This is due to the need to…
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…