69 citations · 258 across the 36 of their papers we have counts for
37 papers
Maximally Robust Satisficing Bayesian Optimization
Samuli Kinnunen, Petrus Mikkola, Antti Niskanen +1
Many design tasks can be cast as black-box function optimization, enabling use of Bayesian optimization to find an ideal design with minimal number of trials. However, often we do…
Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences
Hanlin Yu, RuiKang OuYang, Partha Kaushik +3
Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-bas…
Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification
Bernardo Williams, Harsha Vardhan Tetali, Arto Klami +1
We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison…
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…
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…
Geometric Moment Alignment for Domain Adaptation via Siegel Embeddings
Shayan Gharib, Marcelo Hartmann, Arto Klami
We address the problem of distribution shift in unsupervised domain adaptation with a moment-matching approach. Existing methods typically align low-order statistical moments of th…