# Hyperparameter Optimization

The authors introduce SIMCA: observing that the conditional denoising score-matching objective for diffusion models and the conditional flow-matching objective for flow-matching models are least-squares objectives in the guidance weights at each time step, they reduce guidance-weight tuning in training-free posterior sampling to a two-dimensional linear least-squares problem solved offline by a greedy simulation-based calibration that needs only a single minibatch of sampling trajectories and no retraining; across several inverse problems on FFHQ, ImageNet, CelebA and AFHQ-Cat, the method matches or surpasses state-of-the-art training-free methods and lets diffusion samplers cut steps from 1000 to 50 without significant degradation in reconstruction quality.

The authors introduce SIMCA: observing that the conditional denoising score-matching objective for diffusion models and the conditional flow-matching objective for flow-matching models are least-squares objectives in the guidance weights at each time step, they reduce guidance-weight tuning in training-free posterior sampling to a two-dimensional linear least-squares problem solved offline by a greedy simulation-based calibration that needs only a single minibatch of sampling trajectories and no retraining; across several inverse problems on FFHQ, ImageNet, CelebA and AFHQ-Cat, the method matches or surpasses state-of-the-art training-free methods and lets diffusion samplers cut steps from 1000 to 50 without significant degradation in reconstruction quality.

Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.

Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.

Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.

Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.