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#hyperparameter-optimization

2 curated events

papersTODAY 04:00 UTC

Multi-Objective Hyperparameter Search Using Damped Gauss-Newton Optimization

A new arXiv paper reframes hyperparameter optimization as a numerical optimization problem rather than a sequence of independent trials. The authors propose a multi-objective, damped Gauss-Newton search method that estimates a finite-difference-based model of the objective landscape. The work is a replacement cross-list submission on arXiv's machine learning section.

hyperparameter-optimization/optimization/research/arxiv

papersSEP 10 04:00 UTC

TPE-based hyperparameter optimization and transferability study for ES-HyperNEAT

A new arXiv paper applies a Tree-structured Parzen Estimator method to tune the hyperparameters of ES-HyperNEAT, a neuroevolution technique for growing neural networks. The work analyzes both how effectively this optimization improves results and whether the best settings transfer across problems. The authors note that NEAT-family methods remain highly sensitive to hyperparameter choices.

neuroevolution/hyperparameter-optimization/research