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Found 9 papers, 3 papers with code
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
Quasi-Newton Updating for Large-Scale Distributed Learning
This considerably reduces the computation and communication complexity of the proposed method.
Improved Naive Bayes with Mislabeled Data
Labeling mistakes are frequently encountered in real-world applications.
Embedding Compression for Text Classification Using Dictionary Screening
This leads to a benchmark model, which we then use to obtain the predicted class probabilities for each sample in a dataset.
Network Gradient Descent Algorithm for Decentralized Federated Learning
The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is well balanced, even if the data are distributed heterogeneously.
Factor Normalization for Deep Neural Network Models
Deep neural network (DNN) models often involve features of ultrahigh dimensions.
Hyperparameter Selection for Subsampling Bootstraps
In this article we develop a hyperparameter selection methodology, which can be used to select tuning parameters for subsampling methods.
Automatic, Dynamic, and Nearly Optimal Learning Rate Specification by Local Quadratic Approximation
In each update step, given the gradient direction, we locally approximate the loss function by a standard quadratic function of the learning rate.
Least Squares Approximation for a Distributed System
The finite sample performance and computational efficiency are further illustrated by an extensive numerical study and an airline dataset.
