Summary of Adabatchgrad: Combining Adaptive Batch Size and Adaptive Step Size, by Petr Ostroukhov et al.
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
by Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang, Alexander Gasnikov, Martin Takáč, Dmitry Kamzolov
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel adaptation of Stochastic Gradient Descent (SGD), called AdaBatchGrad, combines an adaptive step size with an adjustable batch size. By increasing the batch size and decreasing the step size, AdaBatchGrad tightens the area of convergence and decreases variance. The paper draws from studies by R. Byrd and J. Nocedal, which introduced testing techniques to assess mini-batch gradient approximations and choose optimal batch sizes. While exact tests converge in O(LR^2/ε) iterations, inexact tests can lead to non-convergence or erratic performance. AdaBatchGrad addresses these challenges by incorporating both adaptive batch and step sizes, enhancing robustness and stability. For exact tests, it converges in O(LR^2/ε) iterations, while for inexact tests, it achieves convergence in O(max{LR^2/ε, σ^2 R2/ε2}) iterations. This makes AdaBatchGrad more robust and computationally efficient than prevailing methods. Experimental results show that introducing adaptive step size and batch size improves regular SGD performance, with AdaBatchGrad outperforming alternative methods, especially for inexact tests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make machine learning algorithms work better. It’s called AdaBatchGrad and it combines two important ideas: adjusting the size of the steps and adjusting the size of the groups of data used to train the algorithm. This helps the algorithm be more reliable and efficient, especially when working with incomplete or uncertain data. The researchers tested their new method and found that it outperformed other methods in many cases. |
Keywords
* Artificial intelligence * Machine learning * Stochastic gradient descent
