Weihua Hu (胡 緯華)

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I am a first-year Computer Science Ph.D. student at Stanford, advised by Prof. Jure Leskovec.

I received a B.E. in Mathematical Engineering in 2016, and an M.S. in Computer Science in 2018, both from the University of Tokyo, where I worked with Prof. Masashi Sugiyama on machine learning and Prof. Hirosuke Yamamoto on information theory. I also worked with Prof. Jun'ichi Tsujii and Prof. Hideki Mima on natural language processing.

[CV] [Google Scholar]

Research Interests

Machine learning for graph-structured data

  1. Developing machine learning methods that can efficiently and effectively handle graph-structured complex data.

  2. Leveraging large amounts of data to learn useful graph representations.

  3. Applying graph representation learning to scientific domains, e.g., chemistry and biology.

Publications

Preprint

  1. Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec.
    Pre-training Graph Neural Networks.
    [arXiv]

2019

  1. Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka.
    How Powerful are Graph Neural Networks?
    International Conference on Learning Representations (ICLR), 2019. (oral)
    [OpenReview] [arXiv] [code]

2018

  1. Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama.
    Does Distributionally Robust Supervised Learning Give Robust Classifiers?
    International Conference on Machine Learning (ICML2018), pp.2034-2042, 2018.
    [arXiv]

  2. Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama.
    Co-teaching: Robust training of deep neural networks with noisy labels.
    Neural Information Processing Systems (NeurIPS2018), to appear, 2018.
    [arXiv]

2017

  1. Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama.
    Learning Discrete Representations via Information Maximizing Self Augmented Training.
    International Conference on Machine Learning (ICML2017), pp.1558-1567, 2017.
    [arXiv][code][talk]

  2. Weihua Hu, Hirosuke Yamamoto, Junya Honda.
    Worst-case Redundancy of Optimal Binary AIFV Codes and their Extended Codes.
    IEEE Transactions on Information Theory, vol.63, no.8, pp.5074-5086, August 2017.
    [arXiv]

  3. Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama.
    Learning from Complementary Labels.
    Neural Information Processing Systems (NeurIPS2017), pp.5644-5654, 2017.
    [arXiv]

2016

  1. Weihua Hu, Hirosuke Yamamoto, Junya Honda.
    Tight Upper Bounds on the Redundancy of Optimal Binary AIFV Codes.
    IEEE International Symposium on Information Theory (ISIT2016), pp.6–10, 2016.
    [paper][slide]

  2. Weihua Hu, Jun'ichi Tsujii.
    A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings.
    The annual meeting of the Association for Computational Linguistics (ACL2016), pp.380–386, 2016.
    [paper][poster][code]

Awards

Work Experiences

Contact

Email: weihuahu [at] stanford.edu
URL: http://web.stanford.edu/~weihuahu/
Github: https://github.com/weihua916/