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Balancing Rates And Variance Via Adaptive Batch-Sizes In First-Order Stochastic Optimization
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Balancing Rates And Variance Via Adaptive Batch-Sizes In First-Order Stochastic Optimization
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-sizes is required for exact a
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-sizes is required for exact a
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