New research tightens bounds on gradient descent for logistic regression · 2 sources tracked

Two new arXiv papers delve into the theoretical underpinnings of gradient descent for logistic regression. The first paper focuses on low-dimensional, separable data, providing tighter bounds on the convergence rate by analyzing the dynamics of the loss function. The second paper examines logistic regression with Gaussian design, characterizing finite-sample estimation performance and establishing faster convergence rates for parameter estimation under different stepsize conditions. Both studies contribute to a deeper understanding of gradient descent's behavior in these specific machine learning contexts. AI

IMPACT Provides theoretical insights into the convergence properties of gradient descent for logistic regression, potentially informing future algorithm development.

RANK_REASON Two academic papers published on arXiv discussing theoretical aspects of gradient descent for logistic regression.

Read on arXiv cs.LG →

• arXiv
• cs.LG
• gradient descent
• logistic regression
• Michael Crawshaw
• Matsumoto
• Mazumdar

• paper
• other

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