Tag
This paper demonstrates that Optuna's constrained Tree-Structured Parzen Estimator (TPE) is a joint density generalization of the c-TPE algorithm, showing its invariance to constraint duplication while independent c-TPE degrades. The authors outline practical tradeoffs and directions for future study.
This paper proposes an automated hyperparameter optimization framework based on Differential Evolution for Latent Factorization of Tensors (LFT) to improve prediction accuracy on large-scale dynamic weighted directed networks, reducing the need for manual tuning.
This paper proposes a staged factorial screening workflow for budget-constrained micro-pretraining, demonstrating that short designed experiments can identify stable hyperparameter penalty directions and support a screen-then-refine strategy.
This paper introduces an auto-research framework using specialist agents to iteratively refine training recipes through an empirical loop of code execution and feedback. The system autonomously improves performance on tasks like Parameter Golf and NanoChat without human intervention by leveraging lineage feedback.