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Sparse Learning with Semi-Proximal-Based Strictly Contractive Peaceman-Rachford Splitting Method
Sen Na, Cho-Jui Hsieh
Minimizing sum of two functions under a linear constraint is what we called splitting problem. This convex optimization has wide applications in machine learning problems, such as…
A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei +1
Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommend…
Communication-Efficient Parallel Block Minimization for Kernel Machines
Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon
Kernel machines often yield superior predictive performance on various tasks; however, they suffer from severe computational challenges. In this paper, we show how to overcome the…
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
Xiangru Lian, Huan Zhang, Cho-Jui Hsieh +2
Asynchronous parallel optimization received substantial successes and extensive attention recently. One of core theoretical questions is how much speedup (or benefit) the asynchron…