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Found 3 papers, 1 papers with code
Data-Driven Performance Guarantees for Classical and Learned Optimizers
We build generalization guarantees for classical optimizers, using a sample convergence bound, and for learned optimizers, using the Probably Approximately Correct (PAC)-Bayes framework.
Learning to Warm-Start Fixed-Point Optimization Algorithms
We introduce a machine-learning framework to warm-start fixed-point optimization algorithms.
Lifted Neural Networks
We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem.
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