69 citations · 1.1k across the 120 of their papers we have counts for
6 papers · 2 filters
Differentiable User Models
Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski
Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed…
Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen +1
Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the…
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Petrus Mikkola, Julien Martinelli, Louis Filstroff +1
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate…
Provably expressive temporal graph networks
Amauri H. Souza, Diego Mesquita, Samuel Kaski +1
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamen…
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
Daolang Huang, Louis Filstroff, Petrus Mikkola +2
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating e…
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
Alexander Nikitin, Samuel Kaski
Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach…