Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP

ICML 2017 Satyen KaleZohar KarninTengyuan LiangDávid Pál

Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a real-valued prediction, receives the true label, and incurs the squared loss. The goal is to design an online learning algorithm with sublinear regret to the best sparse linear predictor in hindsight... (read more)

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