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T10. AutoML, model selection, evaluation design, and experiment tracking
Research on choosing and validating models instead of shipping a single fitted model.
- T10-1Similar problem
Random Search for Hyper-Parameter Optimization
- T10-2Structural parallel
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
- T10-3Structural parallel
Efficient and Robust Automated Machine Learning (auto-sklearn)
- T10-4Candidate approach
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- T10-5Candidate approach
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
- T10-6Candidate approach
Optuna: A Next-generation Hyperparameter Optimization Framework
- T10-7Structural parallel
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
- T10-8Structural parallel
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
- T10-9Similar problem
AMLB: an AutoML Benchmark
- T10-10Similar problem
Statistical Comparisons of Classifiers over Multiple Data Sets
- T10-11Similar problem
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- T10-12Similar problem
Evaluating time series forecasting models: An empirical study on performance estimation methods
- T10-13Structural parallel
OpenML: Networked Science in Machine Learning
- T10-14Structural parallel
Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle
- T10-15Candidate approach
Model Cards for Model Reporting
- T10-16Candidate approach
Datasheets for Datasets
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