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Traditional Machine Learning and AutoML Traditional Ml practice
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Classroom Contents
Evolution of Efficient and Robust AutoML Systems
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- 1 Intro
- 2 Traditional Machine Learning and AutoML Traditional Ml practice
- 3 Focus of This Talk: Progression of Our Ready-To-Use AutoML Systems
- 4 Decades of Work On Predecessors of AutoML European projects
- 5 Auto-WEKA
- 6 Practical Improvement 1: Intermittent Results Retrieval
- 7 Practical Improvement 2: Reduced Search Space
- 8 Multi-Fidelity Optimization: General Overview
- 9 Multi-Fidelity Optimization: Methods
- 10 Multi-Fidelity Optimization: Results
- 11 Portfolio Construction: Why Without Meta Features?
- 12 Portfolio Construction: Problem Definition
- 13 Portfolio Construction: Method
- 14 Portfolio Construction: Theory
- 15 Portfolio Construction: Empirical Results
- 16 Automated policy selection problem Problem. There is no single
- 17 Automated policy selection method
- 18 Automated policy selection: results
- 19 Putting it all together: Auto-sklearn 2.0
- 20 Auto-PyTorch: Multi-Fidelity Optimization
- 21 Auto-PyTorch: Portfolio Construction w/o Meta-Features
- 22 Auto-PyTorch: Comparison Against Previous AutoML Frameworks
- 23 Auto-PyTorch: Evaluation on Image Data
- 24 Regularization Cocktails for SOTA Deep Learning on Tabular Data Combination of 13 different regularizers
- 25 Regularization Cocktails: Evaluation on 40 datasets
