Biography

Weiran Huang (Chinese: 黄维然) is currently a research scientist at Noah's Ark Lab. He received his PhD degree in computer science from IIIS at Tsinghua University, under the supervision of Turing Award winner Prof. Andrew Yao and IEEE Fellow Dr. 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. Before that, he got his BE degree from EE at Tsinghua University. In 2007, he won the gold medal in the 24th Chinese Physics Olympiad (CPhO), and was selected for the National Training Team (国家集训队). His work has been published in top-tier machine learning conferences such as NeurIPS, AISTATS, KDD, CVPR, ICCV, AAAI, IJCAI, etc. He served as a reviewer for ICML, NeurIPS, ICLR, 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 attend the 11th Wu Chien-Shiung Science Camp, and won Osheroff Prize, 2008. [Media Coverage]
  • Gold medal in the 24th Chinese Physics Olympiad (CPhO), and selected for the National Training Team (31 people in China), 2007.

Research Interests

  • Machine Learning Theory: Understanding the fundamental principles of machine learning,e.g., network capacity, optimization method and generalization ability, and providing insights for algorithms.
  • Self-Supervised Learning: Self-supervised learning aims to pre-train a powerful feature extractor through a self-supervised task using a large amount of unlabeled data, such that the learned image representations can be efficiently adapted to downstream tasks.
P.S. We are constantly looking for self-motivated research interns. Please send me your CV if you are interested. (See Details)

Publications

  • Jiaye Teng*, Weiran Huang* and Haowei He*, Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis, AISTATS, 2022. [paper] [more]
  • Weiran Huang*, Mingyang Yi* and Xuyang Zhao*, Towards the Generalization of Contrastive Self-Supervised Learning, arXiv:2111.00743, 2021. [paper] [code] [more]
  • Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li and Liwei Wang, Locally Differentially Private (Contextual) Bandits Learning, NeurIPS, 2020. [paper] [code] [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

  • Invited Talks:
    • Speaker: "Self-Supervised Learning Theory", Alumni Forum of IIIS, Tsinghua University, Mar 2022. [slides]
    • Speaker: "Self-Supervised Learning Theory", Peking University, Mar 2022. [slides] [video]
    • Invited Talk: "The Generalization of Contrastive Self-Supervised Learning", Wangxuan Institute of Computer Technology, Peking University, Dec 2021. [slides]
    • Invited Talk: "The Generalization of Contrastive Self-Supervised Learning", Kuaishou Technology, Dec 2021. [slides]
    • Invited Talk: "The Generalization of Contrastive Self-Supervised Learning", Gaoling School of Artificial Intelligence, Renmin University of China, Dec 2021. [slides]
    • Invited Talk: "The Generalization of Contrastive Self-Supervised Learning", Huawei Theoretical Computer Science Lab, Dec 2021. [slides]
    • Invited Talk: "The Generalization of Contrastive Self-Supervised Learning", National Tianyuan Mathematics Central Center, Wuhan University, Dec 2021. [slides]
    • Speaker: "Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data?", The 2021 Machine Learning and Scientific Computing Workshop, Wuhan University, Aug 2021. [slides]
    • Invited Talk: "Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data?", Department of Computer Science & Technology, Nanjing University, Jun 2021. [slides]
    • Panelist: "Self-Supervised Learning", The 2021 BAAI Conference, Beijing, Jun 2021. [video]
    • Speaker: "Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data?", The 2021 BAAI Conference, Beijing, Jun 2021. [slides] [video]
    • Speaker: "Locally Differentially Private Bandits Learning", Industrial Video Intelligence Summit & Machine Learning Workshop, Nov 2020. [slides]
  • Professional Services:
    • Conference Reviewer: ICML 2022, ICLR 2021, NeurIPS 2021, ICCV 2021, CVPR 2021, 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 & Students:
    Current:
    • Yifei Wang (PhD, Peking University)
    • Jingyi Cui (PhD, Peking University)
    • Xuyang Zhao (PhD, Peking University)
    Past: Mingyang Yi (PhD, Chinese Academy of Sciences), Jiaye Teng (PhD, Tsinghua University), Yuxuan She (PhD, Peking University), Aoxue Li (PhD, Peking University), Peibin Chen (MS, Peking University), Yihong Chen (MS, Tsinghua University), Shifeng Zhang (PhD, Tsinghua University), Yue Liu (PhD, Peking University), Xingqiu He (PhD, University of Electronic Science and Technology of China), Yimin Huang (PhD, Peking University), Junyang Li (MS, Peking University).