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stat.ML2026
Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Yicen Li, Ruiyang Hong, Anastasis Kratsios +2
Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy…
stat.ML2026
Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
Anastasis Kratsios, Gregory Cousins, Haitz Sáez de Ocáriz Borde +2
We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture w…
stat.ML2025
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Anastasis Kratsios, Tin Sum Cheng, Aurelien Lucchi +1
Low-Rank Adaptation (LoRA) has emerged as a widely adopted parameter-efficient fine-tuning (PEFT) technique for foundation models. Recent work has highlighted an inherent asymmetry…