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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 51–75 of 813 papers

TitleStatusHype
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
HomOpt: A Homotopy-Based Hyperparameter Optimization MethodCode1
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationCode1
Hyperparameter optimization in deep multi-target predictionCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Adapters Strike BackCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Kronecker Decomposition for Knowledge Graph EmbeddingsCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
A Critical Assessment of State-of-the-Art in Entity AlignmentCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
Efficient Hyperparameter Optimization for Differentially Private Deep LearningCode1
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
Deep Pipeline Embeddings for AutoMLCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
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