From the 1 of 6 linked papers with an AI index.
6 papers
Maximally Robust Satisficing Bayesian Optimization
Samuli Kinnunen, Petrus Mikkola, Antti Niskanen +1
The paper proposes a Bayesian optimization method that seeks sufficiently good (satisficing) solutions which remain robust to large input perturbations after deployment, rather tha…
Score-Based Density Estimation from Pairwise Comparisons
Petrus Mikkola, Luigi Acerbi, Arto Klami
We study density estimation from pairwise comparisons, motivated by expert knowledge elicitation and learning from human feedback. We relate the unobserved target density to a temp…
Consecutive Preferential Bayesian Optimization
Aras Erarslan, Carlos Sevilla Salcedo, Ville Tanskanen +6
Preferential Bayesian optimization allows optimization of objectives that are either expensive or difficult to measure directly, by relying on a minimal number of comparative evalu…
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Chengkun Li, Bobby Huggins, Petrus Mikkola +1
Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains. We propose normalizing flow regression (NFR), a…
Non-geodesically-convex optimization in the Wasserstein space
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams +4
We study a class of optimization problems in the Wasserstein space (the space of probability measures) where the objective function is nonconvex along generalized geodesics. Specif…
Preferential Normalizing Flows
Petrus Mikkola, Luigi Acerbi, Arto Klami
Eliciting a high-dimensional probability distribution from an expert via noisy judgments is notoriously challenging, yet useful for many applications, such as prior elicitation and…