Optimization for learning
How a learner finds the parameters that minimize a loss.
- Empirical risk minimization
- Gradient descent and variants
- Stochastic gradient methods
- Momentum and acceleration
- Adaptive optimizers
- Second-order and quasi-Newton
- Constrained and proximal methods
- Nonconvex optimization theory
- Learning rate schedules
- Large-batch and distributed optimization
- Zeroth-order and black-box
- Hyperparameter optimization
- Bilevel and meta-optimization
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