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Found 3 papers, 1 papers with code
High Probability Convergence of Stochastic Gradient Methods
Instead, we show high probability convergence with bounds depending on the initial distance to the optimal solution.
META-STORM: Generalized Fully-Adaptive Variance Reduced SGD for Unbounded Functions
There, STORM utilizes recursive momentum to achieve the VR effect and is then later made fully adaptive in STORM+ [Levy et al., '21], where full-adaptivity removes the requirement for obtaining certain problem-specific parameters such as the smoothness of the objective and bounds on the variance and norm of the stochastic gradients in order to set the step size.
Improved Group Robustness via Classifier Retraining on Independent Splits
Given a limited amount of group labels at training time, Just Train Twice (Liu et al., 2021), or JTT in short, is a two-stage method that infers a pseudo group label for every unlabeled example first, then applies group DRO based on the inferred group labels.
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