Biography

Weiran Huang (Chinese: 黄维然) is currently a senior researcher at the AI Theory Group of Noah's Ark Lab. He received his PhD degree in computer science from the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, under the supervision of Prof. Andrew C. Yao and Prof. Wei Chen. He was a visiting scholar at Harvard University hosted by Prof. Yaron Singer, and also served as a research intern at Microsoft Research Asia (MSRA). Before that, he got his BE degree from the Department of Electronic Engineering, Tsinghua University. In 2007, he won a gold medal in the 24th Chinese Physics Olympiad (CPhO), and was chosen to the National Training Team (国家集训队). His work has been published on top conferences such as NeurIPS, ICCV, KDD, AAAI, IJCAI, etc. He also served as a PC or reviewer for NeurIPS, COLT, IJCAI, ASONAM, WINE, JASIST, etc.

AWARDS & HONORS

  • Excellence in the Microsoft Research Asia Internship Program, 2018. [Media Coverage]
  • Outstanding Graduate Student, 2018. [Media Coverage]
  • Excellent Student Leader of Tsinghua, President of IIIS Student Union, 2013. [Media Coverage]
  • Tsinghua-Baidu Scholarship, Tsinghua Social Work Scholarship, Tsinghua Freshman Scholarship, 2008–2013.
  • Top 2 in Tsinghua Mathematical Contest in Modeling, 2012.
  • Excellent work in the Tencent Internet Development Competition, 2012.
  • Invited to the 11th Wu Chien-Shiung Science Camp, and won Osheroff Prize, 2008. [Media Coverage]
  • 1st Prize (gold medal) in the 24th Chinese Physics Olympiad (CPhO), and chosen to the National Training Team (31 people in China), 2007. [Media Coverage]

Research Interests

  • Machine Learning Theory: Learning theory aims to understand the fundamental principles of machine learning, e.g., network capacity, optimization method and generalization ability, and provides insights for algorithms. Theoretical research on machine learning is very important, since it can prevent machine learning from becoming alchemy and guide the design of new algorithms and frameworks.
  • Few-Shot Learning/Federated Learning: Deep learning has achieved great success in various tasks, however, it requires large amounts of labeled data for model training. This severely limits its applications – in many scenarios, collecting a large number of labeled samples is costly, infeasible or even impossible, e.g., medical data and mobile user’s data. To overcome such limitation, how to use only a few labeled samples to learn a model by transferring generic knowledge or information from other domains or other devices becomes more and more important.
P.S. We are constantly looking for self-motivated research interns. Please send me your CV if you are interested. (See Details)

Publications

  • Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li and Liwei Wang, Locally Differentially Private (Contextual) Bandits Learning, NeurIPS, 2020. [paper] [more]
  • Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li and Liwei Wang, Boosting Few-Shot Learning With Adaptive Margin Loss, CVPR, 2020. [paper] [more]
  • Yimin Huang, Weiran Huang, Liang Li and Zhenguo Li, Meta-Learning Pac-Bayes Priors in Model Averaging, AAAI, 2020. [paper] [more]
  • Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen and Zhenguo Li, New Interpretations of Normalization Methods in Deep Learning, AAAI, 2020. [paper] [more]
  • Hanwen Liang*, Shifeng Zhang*, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang and Zhenguo Li, Darts+: Improved Differentiable Architecture Search With Early Stopping, arXiv:1909.06035, 2019. [paper] [media coverage] [more]
  • Aoxue Li*, Tiange Luo*, Tao Xiang, Weiran Huang and Liwei Wang, Few-Shot Learning With Global Class Representations, ICCV, 2019. [paper] [code] [more]
  • Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang and Tie‑Yan Liu, Modeling Local Dependence in Natural Language With Multi-Channel Recurrent Neural Networks, AAAI, oral paper, 2019. [paper] [more]
  • Xiaowei Chen, Weiran Huang, Wei Chen and John CS Lui, Community Exploration: From Offline Optimization to Online Learning, NeurIPS, 2018. [paper] [more]
  • Lichao Sun, Weiran Huang, Philip S Yu and Wei Chen, Multi-Round Influence Maximization, KDD, oral paper (AR: 10.9%), 2018. [paper] [more]
  • Weiran Huang, Jungseul Ok, Liang Li and Wei Chen, Combinatorial Pure Exploration With Continuous and Separable Reward Functions and Its Applications, IJCAI, 2018. [paper] [more]
  • Weiran Huang, Liang Li and Wei Chen, Partitioned Sampling of Public Opinions Based on Their Social Dynamics, AAAI, 2017. [paper] [more]
*Equal contribution authors. (The above list is generated from the .bib file.)

Activities

  • Professional Services:
    • Conference Reviewer: AAAI 2021 (PC), NeurIPS 2020, IJCAI 2020 (PC), ASONAM 2020 (PC), NeurIPS 2019, ASONAM 2019 (PC), COLT 2019, WINE 2017, NIPS 2016.
    • Journal Reviewer: JASIST.
  • Interns and Students Working With Me:
    Current:
    • Yuxuan She (Peking University)
    Past:
    • Aoxue Li (Peking University → Noah's Ark Lab)
    • Peibin Chen (Peking University)
    • Yihong Chen (Tsinghua University → University College London)
    • Shifeng Zhang (Tsinghua University → Noah's Ark Lab)
    • Yue Liu (Peking University → Noah's Ark Lab)
    • Xingqiu He (University of Electronic Science and Technology of China)
    • Yimin Huang (Peking University → Noah's Ark Lab)
    • Junyang Li (Peking University → Noah's Ark Lab)