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New adaptive gradient methods enhance AI model training efficiency and generalization · 3 sources tracked

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new adaptive gradient methods to improve the efficiency and generalization of large-scale model training. The first paper introduces AdAdaGrad and AdAdaGradNorm, which progressively increase batch sizes during training while using AdaGrad and AdaGradNorm, respectively, demonstrating improved training efficiency and model generalization in image classification experiments. The second paper presents AEPG-SPIDER and AEPG, adaptive extrapolated proximal gradient methods with variance reduction for composite nonconvex finite-sum minimization, achieving optimal iteration complexity for finding approximate stationary points and showing superior performance in preliminary experiments. The third paper analyzes a variant of stochastic gradient descent with initial regularization (SGDIR), deriving dimension-free upper bounds on its expected excess risk and showing its performance is comparable to ridge regression in noisy scenarios. AI

IMPACT These advancements in optimization techniques could lead to more efficient training of larger and more complex AI models, potentially improving their performance and generalization capabilities.

RANK_REASON The cluster contains three academic papers detailing new optimization methods for machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New adaptive gradient methods enhance AI model training efficiency and generalization · 3 sources tracked

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The cluster contains three academic papers detailing new optimization methods for machine learning.
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COVERAGE [4]

  1. arXiv stat.ML TIER_1 English(EN) · Caixing Wang, Zhibo Chen, Yue Wang ·

    Adaptive Regularization for Random Features: A Neighboring Early-Stopping Rule with Oracle-Rate Guarantees

    arXiv:2608.25513v1 Announce Type: new Abstract: Random feature methods provide a scalable approximation to kernel ridge regression (KRR), but the regularization parameter that yields the oracle learning rate depends on unknown smoothness and capacity parameters. In this work, we …

  2. arXiv stat.ML TIER_1 English(EN) · Tim Tsz-Kit Lau, Han Liu, Mladen Kolar ·

    AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

    arXiv:2402.11215v4 Announce Type: replace-cross Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-scale model training. Although large-batch training is arguably the dominant …

  3. arXiv cs.CV TIER_1 English(EN) · Ganzhao Yuan ·

    Adaptive Extrapolated Proximal Gradient Methods with Variance Reduction for Composite Nonconvex Finite-Sum Minimization

    arXiv:2502.21099v3 Announce Type: replace-cross Abstract: This paper proposes {\sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing composite nonconvex finite-sum functions. It integrates three acceleration techniques: …

  4. arXiv stat.ML TIER_1 English(EN) · Nabil Kahal\'e ·

    Stochastic gradient descent with initial regularization

    arXiv:2608.22953v1 Announce Type: cross Abstract: We analyze a variant of stochastic gradient descent with initial regularization (SGDIR) and derive dimension-free upper bounds on its expected excess risk for the squared loss. In the noiseless case, we obtain new bounds for both …

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