40 citations · 227 across the 15 of their papers we have counts for
4 papers · 1 filter
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization
Gauthier Guinet, Valerio Perrone, Cédric Archambeau
Bayesian optimization (BO) is a popular method to optimize expensive black-box functions. It efficiently tunes machine learning algorithms under the implicit assumption that hyperp…
Fair Bayesian Optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar +3
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML model…
Cost-aware Bayesian Optimization
Eric Hans Lee, Valerio Perrone, Cedric Archambeau +1
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While B…