23 papers
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
Zahra Rahiminasab, Reza Soumi, Arto Klami +1
Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Exi…
Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
Fanyi Wu, Lihua Niu, Samuel Kaski +1
Bayesian conformal optimisation methods often use the same held-out data both to search for efficient prediction sets and to certify coverage or risk. This coupling is natural for…
Elicitation-Augmented Bayesian Optimization
Alvar Haltia, Ville Hyvönen, Samuel Kaski
Human-in-the-loop Bayesian optimization (HITL BO) methods utilize human expertise to improve the sample-efficiency of BO. Most HITL BO methods assume that a domain expert can quant…
In-Context Multi-Objective Optimization
Xinyu Zhang, Conor Hassan, Julien Martinelli +2
Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such exp…
Online Sharp-Calibrated Bayesian Optimization
Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1
Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions, commonly based on Gaussian process (GP) surrogate models. Its effectiveness reli…
Bayesian Conformal Prediction as a Decision Risk Problem
Fanyi Wu, Veronika Lohmanova, Samuel Kaski +1
We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk control to produce prediction se…