Adaptive online gradients: data-dependent proximal geometry and conditioning

What changed

Developed data-dependent diagonal/full-matrix proximal or conditioning schemes from accumulated gradients, with complementary Adagrad and adaptive-bound formulations improving instance-dependent online regret.

Scientific scope

Developed data-dependent diagonal/full-matrix proximal or conditioning schemes from accumulated gradients, with complementary Adagrad and adaptive-bound formulations improving instance-dependent online regret. Bundles independent2010McMahan/Streeter and2010preliminary/2011Duchi/Hazan/Singer contributions with source-specific author sets, not a fictitious five-author paper or identical algorithms. Competitive/regret statements restrict convex losses, feasible geometry, comparators and gradient assumptions; not global nonconvex deep-learning convergence or minimax improvement without structure.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Bundles independent2010McMahan/Streeter and2010preliminary/2011Duchi/Hazan/Singer contributions with source-specific author sets, not a fictitious five-author paper or identical algorithms. Competitive/regret statements restrict convex losses, feasible geometry, comparators and gradient assumptions; not global nonconvex deep-learning convergence or minimax improvement without structure.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Adaptive Bound Optimization for Online Convex Optimization
• Less Regret via Online Conditioning

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