BANDIT-BASED TECHNIQUES FOR FAIRNESS-AWARE HYPERPARAMETER OPTIMIZATION

In various embodiments, a process for fairness-aware hyperparameter optimization based on bandit-based techniques includes receiving a fairness evaluation metric for evaluating a fairness of a machine learning model to be trained and receiving a performance metric for evaluating performance of the m...

Full description

Saved in:
Bibliographic Details
Main Authors Soares, Carlos Manuel Milheiro de Oliveira Pinto, Bizarro, Pedro Gustavo Santos Rodrigues, Cruz, André Miguel Ferreira da, Saleiro, Pedro dos Santos
Format Patent
LanguageEnglish
Published 13.01.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
Online AccessGet full text

Cover

More Information
Summary:In various embodiments, a process for fairness-aware hyperparameter optimization based on bandit-based techniques includes receiving a fairness evaluation metric for evaluating a fairness of a machine learning model to be trained and receiving a performance metric for evaluating performance of the machine learning model to be trained. The process includes automatically evaluating candidate combinations of hyperparameters of the machine learning model based at least in part on multi-objective optimization including scalarization and using the fairness evaluation metric and the performance metric to select a hyperparameter combination to utilize among the candidate combinations of hyperparameters, wherein evaluating the candidate combinations of hyperparameters of the machine learning model includes automatically and dynamically determining a relative weighting between the fairness evaluation metric and the performance metric. The process includes using the selected hyperparameter combination to train the machine learning model.
Bibliography:Application Number: US202117370747