Decentralised learning with distributed gradient descent and random features
We investigate the generalisation performance of Distributed Gradient Descent with implicit regularisation and random features in the homogenous setting where a network of agents are given data sampled independently from the same unknown distribution. Along with reducing the memory footprint, random...
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| Materiálatiipa: | Conference item |
| Giella: | English |
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Proceedings of Machine Learning Research
2020
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