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I am focusing on finding efficient derivative-free optimization approaches and employing derivative-free optimization on real machine learning applications. Optimization is the core issue in machine learning. With the growing complexity of machine learning tasks, the formulated optimization problems are losing good mathematic properties, e.g., continuous, convex and so on. Especially on reinforcement learning and automatic machine learning (AutoML), plenty of optimization problems are non-convex. The widely used gradient-based optimization techniques, however, are showing limitations for these complex problems. Meanwhile, derivative-free optimization involves kinds of methods that performs optimization through sampling, instead of using gradients, which has great potential for complex problems.
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论文共 21 篇作者统计合作学者相似作者
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arxiv(2022)
PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (2019): 2528-2534
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