11 Projected Newton-type Methods in Machine Learning

By Mark W. Schmidt. 2011

Subfields: Optimization & Training

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In Optimization & Training

1. Nicolas Loizou
2. Simon Lacoste-Julien
3. Issam Hadj Laradji
4. Sharan Vaswani
5. Peter Richtárik
6. Robert Mansel Gower
7. Gauthier Gidel
8. Sebastian U. Stich

Citations in the corpus

• Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence (2020, methodology): “Furthermore, similar to Schmidt et al. (2011), we propose a method to increase the mini-batch size for evaluating the stochastic gradient and guarantee convergence to the optimal solution without interpolation.”