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Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning
In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning.
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to balance its quality and conditioning.
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