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convergence rates

topic3 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
papersTODAY 04:00 UTC

Sharp Rates and a One-Line Fix for Spectral Representation Learning

A new arXiv paper analyzes spectral methods for representation learning, where an encoder is trained once, frozen, and then reused by lightweight probes on downstream tasks. The authors derive tight convergence rates and propose a minimal, one-line correction that determines when off-the-shelf features suffice and when they need adjustment.

research/representation-learning/self-supervised/arxiv