Live briefing
Everything about Hyperparameter optimization
Process of finding the optimal set of variables for a machine learning algorithm
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Hyperparameter optimizationA 5-minute orientation
- Skim a recent paper: Hyperparameter optimization: Foundations, algorithms, best practices, and open challengesInfluential work since 2022
- Pick up a classic book: Gaussian processes for machine learningThe most-reprinted book on the subject
- Read the landmark paper: On hyperparameter optimization of machine learning algorithms: Theory and practiceCited 3,138 times
- Go sideways: Neural architecture searchA closely related topic
- Go sideways: Hyperparameter (machine learning)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
On hyperparameter optimization of machine learning algorithms: Theory and practice
Initializing Bayesian Hyperparameter Optimization via Meta-Learning
Hyperparameter optimization in learning systems
Sherpa: Robust hyperparameter optimization for machine learning
Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning
Weighted Random Search for CNN Hyperparameter Optimization
Be aware of overfitting by hyperparameter optimization!
Fast Bayesian hyperparameter optimization on large datasets
🆕 Influential since 2022
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Hyperparameter Optimization in Machine Learning
Image steganalysis using active learning and hyperparameter optimization
Online Hyperparameter Optimization for Class-Incremental Learning
Hyperparameter Optimization for AST Differencing
📚 Books worth reading
Gaussian processes for machine learningCarl Edward Rasmussen · 2005- 📕Spatially Explicit Hyperparameter Optimization for Neural NetworksMinrui Zheng · 2021
- 📕Hyperparameter Optimization in Machine LearningTanay Agrawal · 2020
Automated Machine LearningAdnan Masood, Ahmed Sherif · 2021
Neural Networks: Tricks of the TradeGrégoire Montavon · 2012- 📕Hyperparameter Optimization in Machine LearningLuca Franceschi, Michele Donini · 2025
Prediction of Chloride Ion Concentration and Durability Assessment of the Interface Zone in Precast-Cast-in-Situ Concrete StructuresHuang Tang Yijia, Wang Yizheng, Li Ziqian, Hai Li · 2026
Linear Algebra and Learning from DataGilbert Strang · 2019
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Neural architecture search Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine…
- Hyperparameter (machine learning) In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters…
- Bayesian optimization Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is…
- Weight-space programming Weight-space programming is an approach in machine learning in which a general-purpose large language model (LLM) reads, interprets and directly writes…
- Learning rate In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration…
- Stochastic gradient descent Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e…
- Federated learning Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaborat…
- Training, validation, and test data sets In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function…