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Efficient Learning using Forward-Backward Splitting

Efficient Learning using Forward-Backward Splitting

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. We describe, analyze, and experiment with a new framework for empirical loss minimization with regularization. Our algorithmic framework alternates between two phases. On each iteration we first perform an unconstrained gradient descent step. We then cast and solve an instantaneous optimization problem that trades off minimization of a regularization term while keeping close proximity to the result of the first phase. This yields a simple yet effective algorithm for both batch penalized risk minimization and online learning. Furthermore, the two phase approach enables sparse solutions when used in conjunction with regularization functions that promote sparsity, such as ℓ1. We... Show More

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