Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.
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Category theory proves prompt-based learning on perfect foundation models works only for representable tasks, fine-tuning solves tasks in the pretext category, and models can represent unseen target-category objects using source-category structure.
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Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.
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On the Power of Foundation Models
Category theory proves prompt-based learning on perfect foundation models works only for representable tasks, fine-tuning solves tasks in the pretext category, and models can represent unseen target-category objects using source-category structure.