arXiv:2606.15832v1 Announce Type: new Abstract: Empirical risk minimization on massive datasets naturally exhibits a nested double finite-sum structure, where $N=nm$ total samples are logically or physically partitioned into $n$ blocks of size $m$ (e.g., in pooled data silos, out-of-core learning, or deliberate stratification). While variance-reduced methods achieve optimal oracle complexities for nonconvex objectives, they suffer from severe scaling bottlenecks in this centralized regime. Recursive estimators, such as PAGE, require periodic global full-gradient refreshes over all $nm$ samples
The paper addresses current scaling bottlenecks in variance-reduced optimization methods, a critical challenge in processing massive and distributed datasets for AI training.
This development can significantly improve the efficiency and scalability of machine learning models trained on large, distributed datasets, impacting fundamental AI capabilities and resource requirements.
New algorithms like SILAGE will enable more memory-efficient and gradient-free nonconvex optimization, mitigating the need for expensive full-gradient refreshes in centralized training regimes.
- · AI research institutions
- · Cloud computing providers (optimizing resource use)
- · Companies with massive proprietary datasets
- · Developers of large-scale AI models
- · Existing less-efficient optimization methods
- · Computational resources (less demand per unit of progress)
Increased ability to train larger, more complex AI models with reduced computational footprint.
Acceleration of research and development in areas reliant on empirical risk minimization on massive distributed datasets.
Potential for new AI applications becoming feasible due to lower computational barriers for training.
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Read at arXiv cs.LG