Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization

Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency. Existing methods use prior information heuristically to accelerate both black-box and multi-fidelity settings, but they lack a characterization of how prior informativeness quantitatively reduces sample complexity. In this work, we provide the first distribution-dependent sample complexity bounds for multi-fidelity HPO with priors through the formal lens of fixed-budget best-arm identification. By modeling priors directly over arm means as configuration performance, we derive explicit, distribution-dependent error bounds that quantify the relationship between priors and evaluation budget. Our analysis shows that informative priors, which concentrate probability mass on near-optimal arms, yield reductions in the number of required evaluations, whereas baseline performance is recovered with uninformative or misleading priors. We conduct proof-of-concept experiments on a synthetic benchmark and on LCBench, a common multi-fidelity HPO benchmark for deep learning, to confirm our theoretical results, achieving up to 90% budget reduction while retaining solution quality. Together, our results provide a principled foundation for prior-guided and compute-efficient green AutoML.

We study hyperparameter optimization (HPO) from a numerical-optimization perspective and propose a multi-objective damped Newton--Gauss--Newton search method. Rather than perturbing each hyperparameter separately or treating model evaluations as independent trials, the method uses performance changes between successive full hyperparameter vectors to construct an iterative secant approximation of the local sensitivity matrix. Each iteration therefore requires only one new full-vector model evaluation while jointly updating all optimized hyperparameters. A Tikhonov-regularized Gauss--Newton system stabilizes the update when the number of hyperparameters exceeds the number of performance objectives. The search is initialized from readily available empirical/default settings of the underlying learner, without requiring a separate initialization search. We evaluate four-dimensional XGBoost HPO on three public classification datasets against exhaustive grid search, random search, and tree-structured Parzen estimator (TPE) optimization. On Breast Cancer Wisconsin, the proposed method matches the best validation accuracy of a 320-configuration grid search with slightly better log loss and ROC--AUC. Across three datasets and three seeds, predictive performance remains competitive with random search and TPE while using substantially fewer search iterations. A supplementary proprietary eight-dimensional threshold-optimization case study further demonstrates joint multi-parameter optimization under competing performance targets and reveals non-monotonic, oscillatory trajectories that motivate damping and best-iterate selection. Overall, the results establish iterative secant sensitivity as an evaluation-efficient local alternative to exhaustive HPO.

Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget. Because every HPO tool relies on a surrogate prior that imparts its own inductive bias, individual tools struggle once problems become sufficiently diverse and drift from these priors. Motivated by the reasoning and generalization capabilities of LLMs, recent work has explored using LLMs for HPO and reports improved per-iteration performance. Yet these methods share two limitations with a common origin: they use the LLM as a single-tool replacement evaluated by iteration count. (i) Deployed in place of prior tools, the LLM is itself constrained by its pretraining objective to one family of inductive-biased proposals; this single-source setup still fails to handle the full diversity of problems. (ii) Per-iteration evaluation ignores that, in real runs, LLM inference or tool execution is paid serially on top of model evaluation every round, so iteration-count gains do not translate into end-to-end wall-clock gains. We present ASAP, an agent-system co-design that addresses both limitations. On the agent side, ASAP uses the LLM to integrate a diverse pool of inductive-biased optimizers and to select among their proposals each round. On the system side, ASAP re-architects the loop to reduce end-to-end wall-clock while preserving regret quality: a prefix-stable prompt maximizes KV-cache reuse across rounds; speculation parallelism hides the remaining LLM and tool latency under model evaluation via a relative-error accept test; and a Self-Tuner adapts the speculation threshold from execution logs off the critical path. Extensive experiments on diverse modern HPO tasks show that ASAP consistently outperforms baselines, underscoring the value of tool integration and agent-system co-design.