Feb. 13, 2024, 5:44 a.m. | Ahmed Khaled Chi Jin

cs.LG updates on arXiv.org arxiv.org

Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We formalize the notion of "tuning-free" algorithms that can match the performance of optimally-tuned optimization algorithms up to polylogarithmic factors given only loose hints on the relevant problem parameters. We consider in particular algorithms that can match optimally-tuned Stochastic Gradient Descent (SGD). When the domain of optimization is bounded, we show tuning-free matching of SGD is …

algorithms cost cs.lg fly free hyperparameter machine machine learning match math.oc notion optimization parameters performance scale stat.ml stochastic

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