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variance reduction

topic2 events
papersTODAY 04:00 UTC

Paper Proposes Multi-block Single-probe Estimator for Coupled Compositional Optimization

A new arXiv preprint introduces a variance reduction technique for finite-sum coupled compositional optimization, a setting where existing single-function estimators such as SPIDER, SARAH and STORM do not directly apply. The authors propose a multi-block, single-probe estimator intended to improve convergence rates in this coupled setting. The work is a theoretical optimization contribution.

optimization/arxiv/variance-reduction/machine-learning
papersSEP 10 04:00 UTC

Variance-Reduced Forward-Reflected-Backward Splitting for Stochastic Composite Inclusions

A new arXiv paper introduces variance-reduction techniques for the forward-reflected-backward splitting method, targeting stochastic composite inclusion problems that may be nonmonotone. The work contrasts its approach with unbiased estimators such as mini-batching, aiming to lower gradient noise in this optimization setting. It covers both unbiased and biased variants of the proposed methods.

optimization/variance-reduction/arxiv/research