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个人简介
In March 2017, I joined the Probabilistic Numerics group where we tackle some of the core aspects underlying the field of Machine Learning from a probabilistic perspective.
A central component of Machine Learning is the training step which involves finding an optimal parameter configuration w.r.t. a loss function. Due to the computational complexity of optimizing the parameters, computationally cheaper 1st order methods (SGD) are often preferred over more accurate 2nd order methods (Newton, CG). I hope to bridge the trade-off between speed and accuracy by extracting and transferring important information for 2nd order methods to make them converge faster, thus reducing the overall computational cost.
A central component of Machine Learning is the training step which involves finding an optimal parameter configuration w.r.t. a loss function. Due to the computational complexity of optimizing the parameters, computationally cheaper 1st order methods (SGD) are often preferred over more accurate 2nd order methods (Newton, CG). I hope to bridge the trade-off between speed and accuracy by extracting and transferring important information for 2nd order methods to make them converge faster, thus reducing the overall computational cost.
研究兴趣
论文共 4 篇作者统计合作学者相似作者
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INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 (2021): 2535-2545
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22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89 (2019): 1448-1457
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