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Found 7 papers, 2 papers with code
Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
In one dimension, we show that a step size less than $1/\lambda$ suffices for global convergence.
A Model-Based Method for Minimizing CVaR and Beyond
We develop a variant of the stochastic prox-linear method for minimizing the Conditional Value-at-Risk (CVaR) objective.
A General Analysis of Example-Selection for Stochastic Gradient Descent
In this paper, we develop a broad condition on the sequence of examples used by SGD that is sufficient to prove tight convergence rates in both strongly convex and non-convex settings.
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
Under an interpolation assumption, we prove that AMSGrad with a constant step-size and momentum can converge to the minimizer at the faster $O(1/T)$ rate for smooth, convex functions.
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)
In this setting, we prove that AMSGrad with constant step-size and momentum converges to the minimizer at a faster $O(1/T)$ rate.
OptiBox: Breaking the Limits of Proposals for Visual Grounding
The problem of language grounding has attracted much attention in recent years due to its pivotal role in more general image-lingual high level reasoning tasks (e. g., image captioning, VQA).
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size.
