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Found 3 papers, 1 papers with code
Personalized Neural Architecture Search for Federated Learning
Federated Learning (FL) is a recently proposed learning paradigm for decentralized devices to collaboratively train a predictive model without exchanging private data.
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection
Bayesian Optimization (BO) is a method for globally optimizing black-box functions.
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
We provide a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm's average performance over the training set and its expected performance.
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