Dai Zhongxiang

I'm a Research Fellow in Department of Computer Science, National University of Singapore. I work on AI/machine learning, advised by Assoc. Prof. Bryan Kian Hsiang Low from NUS and Prof. Patrick Jaillet from MIT.

I'm interested in sequential decision-making under uncertainty, including Bayesian optimization, multi-armed bandit and reinforcement learning. Previously, I also worked on computational neuroscience.

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What's New
  • May 2022: Our paper Federated Neural Bandit uploaded to arXiv.

  • May 2022: Our papers on "Meta-Bayesian Optimization" and "Neural Ensemble Search" are accepted to UAI 2022!

  • May 2022: Our paper "Bayesian Optimization under Stochastic Delayed Feedback" accepted to ICML 2022!

  • Mar 2022: Invited to serve as a reviewer for NeurIPS 2022

  • Feb 2022: Invited to serve as a reviewer for Transactions on Machine Learning Research (TMLR)

  • Jan 2022: Our paper on NAS at Initialization accepted to ICLR 2022!

  • Nov 2021: Invited to serve as a Senior Program Committee member for ICML 2022!

  • Sep 2021: Three papers accepted to NeurIPS 2021!

  • Sep 2021: Invited to serve as a reviewer for IEEE RA-L and ICRA 2022

  • Aug 2021: Won Dean's Graduate Research Excellence Award!

Education

  • National University of Singapore (NUS)   (Aug 2017 - Apr 2021)
  • National University of Singapore (NUS)   (Aug 2011 - Jun 2015)
    • Bachelor of Engineering (Electrical Engineering), First Class Honors
Pre-prints
Publications

2022

2021

2020

2019

  • Bayesian Optimization Meets Bayesian Optimal Stopping.
    Zhongxiang Dai, Haibin Yu, Kian Hsiang Low, and Patrick Jaillet.
    In 36th International Conference on Machine Learning (ICML-19), Long Beach, CA, Jun 9-15, 2019.
    Acceptance rate: 22.6%. [Code, Proceedings]
    Key Words: Bayesian optimization, early stopping, multi-fidelity, reinforcement learning, feature selection.

  • Bayesian Optimization with Binary Auxiliary Information.
    Yehong Zhang, Zhongxiang Dai, and Kian Hsiang Low.
    In Conference on Uncertainty in Artificial Intelligence (UAI-19) , Tel Aviv, Israel, Jul 22-25, 2019.
    Acceptance rate: 26.2% (plenary talk). [Code]
    Key Words: Bayesian optimization, multi-fidelity, entropy search, reinforcement learning.

  • Implicit Posterior Variational Inference for Deep Gaussian Processes.
    Haibin Yu*, Yizhou Chen*, Zhongxiang Dai, Kian Hsiang Low, and Patrick Jaillet.
    In 33th Conference on Neural Information Processing Systems (NeurIPS-19). Vancouver, Canada, Dec 7 - 12, 2019.
    Acceptance rate: 3% (spotlight). [Code]
    Key Words: deep Gaussian processes, generative adversarial networks, game theory.

Awards and Honors
  • Dean's Graduate Research Excellence Award, NUS, School of Computing, 2021

  • Research Achievement Award × 2, NUS, School of Computing, 2019 & 2020

  • Singapore-MIT Alliance for Research and Technology (SMART) Graduate Fellowship, Aug 2017

  • JDDiscovery Population Dynamics Census and Prediction Competition 2018 (annual competition hosted by JD.com): global champion, ranked 1st among > 2,100 teams, Jan 2019 (News in English, News in Chinese)

  • ST Electronics Prize × 2 (the top student in the cohort of Electrical Engineering Year 1 & 2, NUS), Academic Year 2011/2012 & 2012/2013

  • Dean’s List × 5 (top 5% in Electrical Engineering, NUS), 2011-2015

  • Singapore Ministry of Education SM3 scholarship for undergraduate PRC students, 2010

Professional Services
  • Senior Program Committee (SPC) member of ICML 2022
  • Senior Program Committee (SPC) member of IJCAI 2021, Program Committee Board member of IJCAI 2022-2024
  • Program Committee (PC) member/reviewer of
    • ICML (2021, 2022)
    • NeurIPS (2020, 2021, 2022)
    • ICLR (2021, 2022)
    • AAAI (2021, 2022)
    • CoRL (2020, 2021)
    • CVPR (2021, 2022)
    • ICCV (2021)
    • IROS (2021)
    • ICRA (2022)
  • Journal reviewer for
    • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    • Transactions on Machine Learning Research (TMLR)
    • IEEE Robotics and Automation Letters (RA-L)
Academic Talks
  • Bayesian Optimization Meets Bayesian Optimal Stopping, at Singapore-MIT Alliance, Future Urban Mobility Symposium 2019, Jan 28, 2019.
  • Bayesian Optimization Meets Bayesian Optimal Stopping, at Learning and Vision Lab Group Seminar, NUS, ECE, Mar 8, 2019.
  • R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games, at NUS Computing Research Week 2020, Aug 4, 2020 (top 3 student presenter).
Teaching
  • Tutor for CS3244 Machine Learning, NUS School of Computing (Spring 2019)
  • Teaching Assistant for CS1010E Programming Methodology, NUS School of Computing (3 semesters from 2012 to 2014)
Website borrowed from Jon Barron.

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