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Found 5 papers, 2 papers with code
One-shot Distributed Ridge Regression in High Dimensions
To scale up data analysis, distributed and parallel computing approaches are increasingly needed.
Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task
In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Acceleration and momentum are the de facto standard in modern applications of machine learning and optimization, yet the bulk of the work on implicit regularization focuses instead on unaccelerated methods.
WONDER: Weighted one-shot distributed ridge regression in high dimensions
Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment?
Distributed linear regression by averaging
Here we study the performance loss in estimation, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size.
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