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Hyperparameter Optimization

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.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

Papers

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Showing 1–50 of 813 papers

TitleStatusHype
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization ApproachCode7
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML ApplicationsCode6
Cost-Effective Hyperparameter Optimization for Large Language Model Generation InferenceCode4
Aequitas Flow: Streamlining Fair ML ExperimentationCode4
TerraTorch: The Geospatial Foundation Models ToolkitCode4
MetaDE: Evolving Differential Evolution by Differential EvolutionCode3
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded ModesCode3
Personalized Benchmarking with the Ludwig Benchmarking ToolkitCode3
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox OptimizationCode3
Predicting from Strings: Language Model Embeddings for Bayesian OptimizationCode3
Efficient and Robust Automated Machine LearningCode3
Supplementary Material for Efficient and Robust Automated Machine LearningCode3
Multi-objective Asynchronous Successive HalvingCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
Benchmarking Automatic Machine Learning FrameworksCode3
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement LearningCode2
Frugal Optimization for Cost-related HyperparametersCode2
Sequential Model-Based Optimization for General Algorithm ConfigurationCode2
The Neural Hype and Comparisons Against Weak BaselinesCode2
Visual Speech Recognition for Multiple Languages in the WildCode2
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
Towards Learning Universal Hyperparameter Optimizers with TransformersCode2
Out-of-sample scoring and automatic selection of causal estimatorsCode2
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language ModelsCode2
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random FeaturesCode2
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian OptimizationCode2
AutoML: A Survey of the State-of-the-ArtCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Evolutionary Neural AutoML for Deep LearningCode1
Fast Optimizer BenchmarkCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Automated Machine Learning in InsuranceCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?Code1
Deep Pipeline Embeddings for AutoMLCode1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
Adapters Strike BackCode1
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