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stat.ML2025
When is Fine-Tuning Scalable? Certified Recovery from Noisy Data and Linear-Cost Fine-Tuning on Structured Task Shifts
Anastasis Kratsios, Tin Sum Cheng, Daniel Roy +1
Transformers often transfer across related tasks with little fine-tuning; we study this question in noisy nonparametric regression for continuous maps ,…
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…