Author

P. Mikkola

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#machine learning Preprint Sep 2026

Uncertainty-Normalized Margins for Direct Preference Optimization

Direct preference optimization (DPO) models binary preferences through a Bradley-Terry model with a common noise scale, without explicitly accounting for preference strength or prompt-dependent uncertainty from human feedback. We introduce uncertainty-normalized margin DPO (UNM-DPO), which combines strength-dependent m...

Sadegh Khorasani, P. Mikkola, Matthias Grossglauser · 0 citations

Jul 2026

Maximally Robust Satisficing Bayesian Optimization

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 not actually need the optimum but instead a sufficiently good solution is enough, for instance a material that is durable enough for its int...

Samuli Kinnunen, P. Mikkola, Antti Niskanen et al. · arXiv.org · 0 citations