Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning

Sanae Lotfi; Tiphaine Bonniot de Ruisselet; Dominique Orban; Andrea Lodi

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arXiv · 2111.14761

Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning

Sanae Lotfi·Tiphaine Bonniot de Ruisselet·Dominique Orban·Andrea Lodi

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Abstract

In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning. In the first-order case, we propose a framework of transition from deterministic or semi-deterministic to stochastic quadratic regularization methods. We leverage the two-phase nature of stochastic optimization to propose a novel first-order algorithm with adaptive sampling and adaptive step size. In the second-order case, we propose a novel stochastic damped L-BFGS method that improves on previous algorithms in the highly nonconvex context of deep learning. Both algorithms are evaluated on well-known deep learning datasets and exhibit promising performance.

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BibTeX RIS

Sanae Lotfi, Tiphaine Bonniot de Ruisselet, Dominique Orban, Andrea Lodi. 2021-11-29. Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning. https://arxiv.org/abs/2111.14761

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