Author

Sadegh Khorasani

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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

#artificial intelligence Preprint Sep 2026

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive the covariance of the observed variables and introduce marginal quasi-equivalence, which...

Sadegh Khorasani, Ali Najar, Saber Salehkaleybar et al. · 0 citations