A cumulative dissertation demonstrating meta-learned surrogates and synthetic data generators for automated model selection, finetuning, augmentation, and reinforcement-learning environments.
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
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Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning
A cumulative dissertation demonstrating meta-learned surrogates and synthetic data generators for automated model selection, finetuning, augmentation, and reinforcement-learning environments.