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Jiyan Yang
Sub-sampled Newton Methods with Non-uniform Sampling
Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney
We consider the regime where n d.
Feature-distributed sparse regression: a screen-and-clean approach
Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun
Most existing approaches to distributed sparse regression assume the data is partitioned by samples. However, for high-dimensional data (D N), it is more natural to partition the data by features. We propose an algorithm to distributed sparse regression when the data is partitioned by features rather than samples. Our approach allows the user to tailor our general method to various distributed computing platforms by trading-off the total amount of data (in bits) sent over the communication network and the number of rounds of communication.