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Poster
Searching to Exploit Memorization Effect in Learning with Noisy Labels
QUANMING YAO · Hansi Yang · Bo Han · Gang Niu · James Kwok

Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 07:00 PM -- 07:45 PM (PDT) @ None #None

Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memorization effect is a hard problem. In this paper, motivated by the success of automated machine learning (AutoML), we model this issue as a function approximation problem. Specifically, we design a domain-specific search space based on general patterns of the memorization effect and propose a novel Newton algorithm to solve the bi-level optimization problem efficiently. We further provide a theoretical analysis of the algorithm, which ensures a good approximation to critical points. Experiments are performed on both benchmark and real-world data sets. Results demonstrate that the proposed method is much better than the state-of-the-art noisy-label-learning approaches, and also much more efficient than existing AutoML algorithms.

Author Information

QUANMING YAO (4Paradigm)

Dr. Quanming Yao is currently a leading researcher in 4Paradigm and managing the company's research group. He obtained his Ph.D. degree at the Department of Computer Science and Engineering at Hong Kong University of Science and Technology (HKUST) in 2018 and received his bachelor degree at HuaZhong University of Science and Technology (HUST) in 2013. He is Qiming Star (HUST, 2012), Tse Cheuk Ng Tai Research Excellence Prize (CSE, HKUST, 2014-2015), Google Fellowship (machine learning, 2016) and Ph.D. Research Excellence Award (School of Engineering, HKUST, 2018-2019). He has 23 top-tier journal and conference papers, including ICML, NeurIPS, JMLR, TPAMI, KDD, ICDE, CVPR, IJCAI, and AAAI; he was an outstanding reviewer of Neurocomputing in 2017; served as program committee of many prestigious conferences, including ICML, NeurIPS, CVPR, AAAI, and IJCAI; one of the committees of AutoML competition in NeurIPS-2018, IJCNN-2019 and IJCAI-2019.

Hansi Yang (Tsinghua)
Bo Han (HKBU / RIKEN)
Gang Niu (RIKEN)
James Kwok (Hong Kong University of Science and Technology)

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