Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization

• URL: http://arxiv.org/abs/2602.08774v1
• Date: Mon, 09 Feb 2026 15:15:52 GMT
• Title: Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization
• Authors: Nicolás Villagrán Prieto, Eduardo C. Garrido-Merchán,
• Abstract summary: We formalize the idea of default hyperparameters as informative starting points for optimization.<n>We conduct an empirical evaluation spanning three BO back-ends, three model families, and five benchmark datasets.<n>Our results provide no evidence that default hyper parameters encode useful directional information for optimization.
• Score: 1.3320917259299652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian Optimization (BO) is a standard tool for hyperparameter tuning thanks to its sample efficiency on expensive black-box functions. While most BO pipelines begin with uniform random initialization, default hyperparameter values shipped with popular ML libraries such as scikit-learn encode implicit expert knowledge and could serve as informative starting points that accelerate convergence. This hypothesis, despite its intuitive appeal, has remained largely unexamined. We formalize the idea by initializing BO with points drawn from truncated Gaussian distributions centered at library defaults and compare the resulting trajectories against a uniform-random baseline. We conduct an extensive empirical evaluation spanning three BO back-ends (BoTorch, Optuna, Scikit-Optimize), three model families (Random Forests, Support Vector Machines, Multilayer Perceptrons), and five benchmark datasets covering classification and regression tasks. Performance is assessed through convergence speed and final predictive quality, and statistical significance is determined via one-sided binomial tests. Across all conditions, default-informed initialization yields no statistically significant advantage over purely random sampling, with p-values ranging from 0.141 to 0.908. A sensitivity analysis on the prior variance confirms that, while tighter concentration around the defaults improves early evaluations, this transient benefit vanishes as optimization progresses, leaving final performance unchanged. Our results provide no evidence that default hyperparameters encode useful directional information for optimization. We therefore recommend that practitioners treat hyperparameter tuning as an integral part of model development and favor principled, data-driven search strategies over heuristic reliance on library defaults.

Related papers

• Informed Initialization for Bayesian Optimization and Active Learning [13.105080815344174]
  We propose a novel acquisition strategy that balances predictive uncertainty reduction with hyperparameter learning using information-theoretic principles.<n>We demonstrate its effectiveness through extensive experiments in active learning and few-shot BO.
  arXiv  Detail & Related papers  (2025-10-27T15:05:12Z)
• On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization [57.179679246370114]
  A potential limitation of existing methods is the bias inherent in most perturbation estimators unless a stepsize is proposed.<n>We propose a novel family of unbiased gradient scaling estimators that eliminate bias while maintaining favorable construction.
  arXiv  Detail & Related papers  (2025-10-22T18:25:43Z)
• From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models [90.45197506653341]
  Large reasoning models generate intermediate reasoning traces before producing final answers.<n> aligning LRMs with human preferences, a crucial prerequisite for model deployment, remains underexplored.<n>A common workaround optimized a single sampled trajectory, which introduces substantial gradient variance from trace sampling.
  arXiv  Detail & Related papers  (2025-10-06T17:58:01Z)
• Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback [0.0]
  We propose Quantile-Scaled Bayesian Optimization (QS-BO), a rank-based optimization framework.<n> QS-BO converts ranks into heteroscedastic Gaussian targets through a quantile-scaling pipeline.<n>Results demonstrate that QS-BO consistently achieves lower objective values and exhibits greater stability across runs.
  arXiv  Detail & Related papers  (2025-09-28T11:03:18Z)
• Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems [4.0148499400442095]
  This paper deals with the identification of linear derivation systems, where the unknowns include system coefficients and noise variances.<n>A sample-efficient global optimization method based on Bayesian optimization is proposed.<n>Experiments show that the EGP-based BO consistently outperforms MLE via steady-state filtering and expectation-maximization.
  arXiv  Detail & Related papers  (2025-03-09T01:38:21Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation [44.53678257757108]
  We propose a new BO method that can sub-linearly converge to the objective function's global optimum. Our method uses a multi-armed bandit technique (EXP3) to add random data points to the BO process. We demonstrate empirically that our method outperforms existing approaches on various synthetic and real-world problems.
  arXiv  Detail & Related papers  (2023-06-12T03:35:45Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• Towards Learning Universal Hyperparameter Optimizers with Transformers [57.35920571605559]
  We introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction. Our experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates.
  arXiv  Detail & Related papers  (2022-05-26T12:51:32Z)
• Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization [39.824086260578646]
  This paper presents a novel trusted-maximizers entropy search (TES) acquisition function. It measures how much an input contributes to the information gain on a query over a finite set of trusted maximizers.
  arXiv  Detail & Related papers  (2021-07-30T07:25:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.