Hyperparameter Optimization
279 papers with code • 1 benchmarks • 3 datasets
Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.
Libraries
Use these libraries to find Hyperparameter Optimization models and implementations
NAS-Bench-201
NAS-Bench-101
PMLB
Latest papers with no code
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches.
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand.
A machine learning workflow to address credit default prediction
Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention.
Transformers for Low-Resource Languages:Is Féidir Linn!
The Transformer model is the state-of-the-art in Machine Translation.
Exploratory Landscape Analysis for Mixed-Variable Problems
We provide a comprehensive juxtaposition of the results based on these different techniques.
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks
Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process.
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization
Comprehensive study on FlexHB shows that (1) our fine-grained fidelity method considerably enhances the efficiency of searching optimal configurations, (2) our FlexBand framework (self-adaptive allocation of SH brackets, and global ranking of configurations in both current and past SH procedures) grants the algorithm with more flexibility and improves the anytime performance.
Universal Link Predictor By In-Context Learning on Graphs
In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
Glocal Hypergradient Estimation with Koopman Operator
Through numerical experiments of hyperparameter optimization, including optimization of optimizers, we demonstrate the effectiveness of the glocal hypergradient estimation.
