Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
A novel error analysis is provided that provides substantially sharper bounds for products of operators, thereby significantly relaxing existing restrictions on the maximum number of local machines while retaining optimal learning rates for the distributed kernel-based robust gradient descent algorithm.
Jun-Yi Meng, Zheng-Chu Guo, Yuan Mao
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