4 papers
FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
Marcel Hedman, Emily Alger, Brieuc Lehmann +2
Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined by biases already present in…
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…
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…
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…