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
Most implemented papers
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is coupled with a neural predictor.
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
huawei-noah/hebo
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Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Standard federated optimization methods successfully apply to stochastic problems with single-level structure.
GPT Takes the Bar Exam
Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice.
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
Large Language Models (LLMs) have sparked significant interest in their generative capabilities, leading to the development of various commercial applications.
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn
Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library.
Gradient-based Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable.
Efficient and Robust Automated Machine Learning
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts.
