New analysis unifies convergence proofs for SAG, SAGA, and IAG algorithms

Researchers have developed a unified convergence analysis for SAG, SAGA, and IAG algorithms, which are commonly used in large-scale machine learning. This new analysis uses a novel Lyapunov function and concentration tools to establish bounds on delays caused by stochastic sub-sampling. The resulting proof is concise and modular, offering high-probability bounds for SAG and SAGA that can be extended to non-convex objectives. Additionally, this technique yields improved convergence rates for the IAG algorithm. AI

IMPACT Provides a more efficient and unified theoretical understanding for optimization algorithms used in large-scale machine learning.

RANK_REASON The cluster contains an academic paper detailing a new theoretical analysis of existing machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Aritra Mitra
• SAGA

• paper
• other

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