Multi-Objective Population Based Training
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefore introduce a multi-objective version of PBT, MO-PBT. Our experiments on diverse multi-objective hyperparameter optimization problems (Precision/Recall, Accuracy/Fairness, Accuracy/Adversarial Robustness) show that MO-PBT outperforms random search, single-objective PBT, and the state-of-the-art multi-objective hyperparameter optimization algorithm MO-ASHA.
Code (1)
Tasks
Adversarial RobustnessClassificationFairnessHyperparameter OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable. This paper proposes a population-ba…
Efficient Multi-Objective Neural Architecture Search via Pareto Dominance-based Novelty Search
Neural Architecture Search (NAS) aims to automate the discovery of high-performing deep neural network architectures. Traditional objective-based NAS approaches typically optimize a certain performance metric (e.g., pred…
DiversityNeural Architecture SearchStochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objectiv…
Evolutionary AlgorithmsStochastic Population Update Provably Needs An Archive in Evolutionary Multi-objective Optimization
Evolutionary algorithms (EAs) have been widely applied to multi-objective optimization, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually perfor…
Evolutionary AlgorithmsSolution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate …