Fine-grained theoretical analysis of stochastic bilevel optimization algorithms, examining stability and generalization bounds. Contributes to foundational understanding of optimization techniques used in modern machine learning systems.
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Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
New stability and generalization bounds for stochastic bilevel optimization provide the first rigorous guarantees for meta-learning and hyperparameter tuning at scale.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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