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  • title: Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
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            Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
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            Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

            Jul 12, 2020

            Speakers

            ZG

            Zhishuai Guo

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            ML

            Mingrui Liu

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            ZY

            Zhuoning Yuan

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            About

            In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been investigated extensively in deep learning, they are not directly applicable to stochastic AUC maximization with deep neural networks due to its striking differences from standard loss minimization problems (e.g., cross-entropy). Towards addressing this challenge, we propose and analyze a communication-efficient distri…

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            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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