1 citations · 1 across the 3 of their papers we have counts for
6 papers
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 …
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…
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
Alessio Arcudi, Davide Sartor, Alberto Sinigaglia +2
This paper introduces MANGO (Multilayer Abstraction for Nested Generation of Options), a novel hierarchical reinforcement learning framework designed to address the challenges of l…
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations
Davide Sartor, Alberto Sinigaglia, Gian Antonio Susto
Conventional techniques for imposing monotonicity in MLPs by construction involve the use of non-negative weight constraints and bounded activation functions, which pose well-known…
Simple and Effective Specialized Representations for Fair Classifiers
Alberto Sinigaglia, Davide Sartor, Marina Ceccon +1
Fair classification is a critical challenge that has gained increasing importance due to international regulations and its growing use in high-stakes decision-making settings. Exis…
Fault Identification Enhancement with Reinforcement Learning (FIERL)
Valentina Zaccaria, Davide Sartor, Simone Del Favero +1
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control inpu…