arXiv Machine Learning
Multi-objective Hyperparameter Optimization with Expert Priors
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jun 17
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
arXiv:2404. 01965v3 Announce Type: replace-cross Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets.
arXiv AI
Aug 21
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
arXiv:2406. 09770v2 Announce Type: replace-cross Abstract: Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models.
arXiv Machine Learning
Aug 3
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
arXiv AI
Sep 10
A Better Spur Should Start From Each Objective
arXiv:2609.08211v1 Announce Type: new Abstract: Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing...
arXiv Machine Learning
Jun 30
Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization
arXiv:2606. 29521v1 Announce Type: new Abstract: Deep learning problems rarely involve objectives that are equal in importance.
arXiv AI
Jun 2
Rethinking Evaluation Paradigms in IBP-based Certified Training
arXiv:2606. 02134v1 Announce Type: cross Abstract: Deep neural networks achieve strong performance on many supervised learning tasks but remain vulnerable to adversarial perturbations.