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Adaptive Batch Size for Privately Finding Second-order Stationary Points Apple Machine Learning Research

​[[{“value”:”There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear whether privately finding an SOSP is more challenging than finding an FOSP. Specifically, Ganesh et al. (2023) claimed that an αalphaα-SOSP can be found with α=O~(1n1/3+(dnϵ)3/7)alpha=tilde{O}(frac{1}{n^{1/3}}+(frac{sqrt{d}}{nepsilon})^{3/7})α=O~(n1/31​+(nϵd​​)3/7), where nnn is the dataset size, ddd is the dimension, and ϵepsilonϵ is the differential privacy parameter.
However, a recent analysis revealed an issue…”}]] [[{“value”:”There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear whether privately finding an SOSP is more challenging than finding an FOSP. Specifically, Ganesh et al. (2023) claimed that an αalphaα-SOSP can be found with α=O~(1n1/3+(dnϵ)3/7)alpha=tilde{O}(frac{1}{n^{1/3}}+(frac{sqrt{d}}{nepsilon})^{3/7})α=O~(n1/31​+(nϵd​​)3/7), where nnn is the dataset size, ddd is the dimension, and ϵepsilonϵ is the differential privacy parameter.
However, a recent analysis revealed an issue…”}]]  Read More  

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