From the 1 of 16 linked papers with an AI index.
16 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…
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…
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…
Learning Geometry and Topology via Multi-Chart Flows
Hanlin Yu, Søren Hauberg, Marcelo Hartmann +2
Real world data often lie on low-dimensional Riemannian manifolds embedded in high-dimensional spaces. This motivates learning degenerate normalizing flows that map between the amb…
On the Identifiability of Tensor Ranks via Prior Predictive Matching
Eliezer da Silva, Arto Klami, Diego Mesquita +1
Selecting the latent dimensions (ranks) in tensor factorization is a central challenge that often relies on heuristic methods. This paper introduces a rigorous approach to determin…
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…