arXiv Machine Learning
Multi-Objective Hyperparameter Search via Damped Gauss--Newton Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 17
Selecting Hyperparameters for Tree-Boosting
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
arXiv Machine Learning
Jun 30
Structured Proper Loss Geometries for Multiclass Classification: Theory and Controlled Empirical Evaluation
arXiv:2606. 29471v1 Announce Type: new Abstract: Strictly proper scoring rules identify the true conditional class distribution at population level, but their curvature can alter optimization and finite-sample behavior.
arXiv Machine Learning
Sep 24
tidyHEBO: Robust General-Purpose Bayesian Optimization with Model-Consistent Warping and Pareto Search
tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
arXiv Machine Learning
1d ago
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
The paper investigates how treating the optimization objective as a design variable can improve Remaining Useful Life (RUL) prediction in predictive maintenance. Five model architectures are compared under single‑objective and multi‑objective hyperparameter optimization, with the latter using NSGA‑II and Entropy‑CRITIC weighting to balance accuracy and prediction timeliness. Results on NASA C‑MAPSS and BackBlaze datasets show that multi‑objective optimization reduces directional imbalance in predictions and can alter model rankings, highlighting the importance of objective choice in RUL modeling.
arXiv Machine Learning
Jun 2
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
arXiv Machine Learning
Jun 25
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.