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cs.LG2026
Parameter-Efficient Generative Modeling with Controlled Vector Fields
Peyman Morteza
We introduce a continuous-time generative modeling framework, motivated by the Chow-Rashevskii theorem, that builds expressive flows from a small set of fixed vector fields and lea…
cs.LG2023
Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms
Peyman Morteza
We develop a mathematical framework to address a broad class of metric and preference learning problems within a Hilbert space. We obtain a novel representer theorem for the simult…