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Bayesian posterior approximation

topic1 events
papersSEP 11 04:00 UTC

Paper Proposes Langevin Gradient Descent for Data-Driven Hyperparameter Tuning

A new arXiv preprint introduces the Langevin Gradient Descent Algorithm (LGD), which approximates the mean of a posterior distribution defined by a regression problem's loss function and regularizer in order to tune gradient descent hyperparameters. The authors frame this as a learning-to-learn problem and provide generalization guarantees for the approach. The work targets data-driven tuning of optimization settings rather than hand-selected values.

research/optimization/machine-learning/arxiv