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Found 4 papers, 1 papers with code
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market
The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence of multiple well-trained models to improve the performance of the resultant model through one-shot federated learning and ensemble learning in a data-free manner.
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality
Optimization and generalization are two essential aspects of statistical machine learning.
Short optimization paths lead to good generalization
Through our approach, we show that, with a proper initialization, gradient flow converges following a short path with an explicit length estimate.
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
In this paper, we study the effect of the adaptation learning rate in meta-learning with mixed linear regression.
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