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stat.ML2026
How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Aditya Kulkarni, Davide Sartor, Carlos Soto +1
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than …
stat.ML2026
Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds
Davide Sartor, Meghan E. Huber, Donghyun Kim +1
Bayesian Optimization (BO) has become an established methodology for minimizing black-box functions of a vector input. Often, however, this parameter vector arises from the discret…