Jul 12, 2020
Ordered Weight L_1-Norms (OWL) is a new family of regularizers for high-dimensional sparse regression. However, due to the non-separable penalty, existing algorithms are either invalid or inefficient when either the size of the feature or sample is large. To address this challenge, we propose the first safe screening rule for the OWL regularized regression, which effectively avoids the updates of the parameters whose coefficients must be zeros. Moreover, we prove the proposed screening rule can be safely applied to the standard proximal gradient methods. More importantly, our screening rule can also be safely applied to stochastic proximal gradient methods in large-scale learning, which is the first safe screening rule in the stochastic setting. Experimental results on a variety of datasets show that the screening rule leads to a significant computation gain without any loss of accuracy, compared to exiting competitive algorithms.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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