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News/Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
arxiv
PublishedApril 2, 2026 at 4:00 AM
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Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization

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arXiv:2604.00260v1 Announce Type: new Abstract: Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for arbitrary within-epoch permutations. In particular, random reshuffling

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Mentioned models
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  • 01
    large language model (LLM)
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Read original ↗All from arxiv →
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#optimization#machine-learning#research#algorithms

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