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High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
We provide a rigorous theoretical guarantee for the confidence interval, demonstrating that the coverage is approximately exact with an explicit convergence rate and allowing for high confidence level inference.
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Stochastic Gradient Descent (SGD) is one of the simplest and most popular algorithms in modern statistical and machine learning due to its computational and memory efficiency.
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