What are the latest analyses related to stochastic gradient descent algorithms?
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Recent analyses related to stochastic gradient descent (SGD) algorithms have focused on various aspects. One study investigated the relation between the variance of neural weights and the landscape flatness of the loss function under SGD, bridging the gap between statistical mechanics and artificial intelligence . Another analysis examined the convergence rate of biased SGD, considering computation errors and providing theoretical formulas for convergence properties . A different approach introduced the stochastic gradient process as a continuous-time representation of SGD, studying its convergence properties and conditions for exponential ergodicity . Additionally, a unified convergence analysis was proposed for stochastic gradient descent-ascent (SGDA) methods, covering a wide range of variants and providing new methods for min-max optimization and variational inequalities problems . Lastly, an investigation into the asymptotic behaviors of gradient descent algorithms in the context of stochastic optimization revealed a joint computational and statistical asymptotic analysis framework, identifying factors influencing the performance of stochastic gradient descent for non-convex optimization problems .
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Open access•Journal Article | The paper discusses the asymptotic behaviors of gradient descent algorithms, including stochastic gradient descent, in the context of stochastic optimization. It provides a unified framework for joint computational and statistical asymptotic analysis. However, it does not specifically mention the latest analyses related to stochastic gradient descent algorithms. |
Proceedings Article 15 Feb 2022 20 Citations | The paper proposes a unified convergence analysis for various stochastic gradient descent-ascent methods, including new variants such as L-SVRGDA, QSGDA, DIANA-SGDA, VR-DIANA-SGDA, and SEGA-SGDA. |
13 Citations | The paper provides a convergence rate analysis for biased stochastic gradient descent (SGD) algorithms, considering computation errors in individual gradient updates. It does not mention any other latest analyses related to stochastic gradient descent algorithms. |
| The paper discusses the analysis of stochastic gradient descent (SGD) near fixed points using a dynamic decomposition method. It investigates the anomaly relation between weight variance and loss function flatness under SGD. |
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