40 citations · 227 across the 15 of their papers we have counts for
17 papers
A Simple and Fast Baseline for Tuning Large XGBoost Models
Sanyam Kapoor, Valerio Perrone
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can furt…
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Eric Hans Lee, David Eriksson, Valerio Perrone +1
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions. BO budgets are typically given in iterations, which implicitly assumes each…
A multi-objective perspective on jointly tuning hardware and hyperparameters
David Salinas, Valerio Perrone, Olivier Cruchant +1
In addition to the best model architecture and hyperparameters, a full AutoML solution requires selecting appropriate hardware automatically. This can be framed as a multi-objectiv…
Automatic Termination for Hyperparameter Optimization
Anastasia Makarova, Huibin Shen, Valerio Perrone +5
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configuration…
Lexical semantic change for Ancient Greek and Latin
Valerio Perrone, Simon Hengchen, Marco Palma +3
Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associatin…
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,…