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 problems, including Bayesian optimization, multi-armed bandits and reinforcement learning, as well as their applications in AI for Science and autoML. Previously, I also worked on computational neuroscience.

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What's New
  • Jan 2023: 2 papers accepted to ICLR 2023!

  • Dec 2022: Invited to serve as a reviewer for ICML 2023 and UAI 2023

  • Dec 2022: our paper on Recursive Reasoning-Based Training-Time Adversarial ML accepted to the AI Journal!

  • Sep 2022: 2 papers accepted to NeurIPS 2022!

  • Aug 2022: Invited to serve as a reviewer for AAMAS 2023

  • Aug 2022: Invited to serve as a reviewer for AAAI 2023

  • Jul 2022: Invited to serve as a reviewer for ICLR 2023

  • Jul 2022: Invited to serve as a reviewer for CoRL 2022

  • 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!

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
* denotes equal contribution, denotes corresponding author.
  1. Federated Neural Bandits.
    Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Kian Hsiang Low and Patrick Jaillet.
    ICLR 2023. Acceptance rate: 31.8%.

  2. Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation.
    Yao Shu*, Zhongxiang Dai*, Weicong Sng, Arun Verma, Patrick Jaillet and Kian Hsiang Low.
    ICLR 2023. Acceptance rate: 31.8%.

  3. Recursive Reasoning-Based Training-Time Adversarial Machine Learning.
    Yizhou Chen, Zhongxiang Dai, Haibin Yu, Kian Hsiang Low and Teck-Hua Ho.
    In Artificial Intelligence (Special Issue on Risk-Aware Autonomous Systems: Theory and Practice), 2023.

  4. Sample-Then-Optimize Batch Neural Thompson Sampling.
    Zhongxiang Dai, Yao Shu, Kian Hsiang Low and Patrick Jaillet.
    NeurIPS 2022. Acceptance rate: 25.6%. [arXiv, Code]

  5. Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.
    Yao Shu, Zhongxiang Dai, Zhaoxuan Wu and Kian Hsiang Low.
    NeurIPS 2022. Acceptance rate: 25.6%. [arXiv]

  6. Bayesian Optimization under Stochastic Delayed Feedback.
    Arun Verma*, Zhongxiang Dai* and Kian Hsiang Low.
    ICML 2022. Acceptance rate: 21.9%.

  7. On Provably Robust Meta-Bayesian Optimization.
    Zhongxiang Dai, Yizhou Chen, Haibin Yu, Kian Hsiang Low and Patrick Jaillet.
    UAI 2022. Acceptance rate: 32.3%. [OpenReview]

  8. Neural Ensemble Search via Bayesian Sampling.
    Yao Shu, Yizhou Chen, Zhongxiang Dai and Kian Hsiang Low.
    UAI 2022. Acceptance rate: 32.3%. [OpenReview]

  9. NASI: Label- and Data-agnostic Neural Architecture Search at Initialization.
    Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi and Kian Hsiang Low.
    ICLR 2022. Acceptance rate: 32.3%. [OpenReview, arXiv]

  10. Differentially Private Federated Bayesian Optimization with Distributed Exploration.
    Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet.
    NeurIPS 2021. Acceptance rate: 26%. [OpenReview, Code]

  11. Optimizing Conditional Value-At-Risk of Black-Box Functions.
    Quoc Phong Nguyen, Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet.
    NeurIPS 2021. Acceptance rate: 26%. [OpenReview, Code]

  12. Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee.
    Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan and Kian Hsiang Low.
    NeurIPS 2021. Acceptance rate: 26%. [OpenReview, Code]

  13. Value-at-Risk Optimization with Gaussian Processes.
    Quoc Phong Nguyen, Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet.
    ICML 2021. Acceptance rate: 21.4%. [Proceedings, Code]

  14. Federated Bayesian Optimization via Thompson Sampling.
    Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet.
    NeurIPS 2020. Acceptance rate: 20.1%. [Code, Proceedings]

  15. R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games.
    Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet and Teck-Hua Ho.
    ICML 2020. Acceptance rate: 21.8%. [Code, Proceedings, Video]

  16. Private Outsourced Bayesian Optimization.
    Dmitrii Kharkovskii, Zhongxiang Dai and Kian Hsiang Low.
    ICML 2020. Acceptance rate: 21.8%. [Code, Proceedings, Video]

  17. Bayesian Optimization Meets Bayesian Optimal Stopping.
    Zhongxiang Dai, Haibin Yu, Kian Hsiang Low, and Patrick Jaillet.
    ICML 2019. Acceptance rate: 22.6%. [Code, Proceedings]

  18. Bayesian Optimization with Binary Auxiliary Information.
    Yehong Zhang, Zhongxiang Dai, and Kian Hsiang Low.
    UAI 2019. Acceptance rate: 26.2% (plenary talk). [Code]

  19. Implicit Posterior Variational Inference for Deep Gaussian Processes.
    Haibin Yu*, Yizhou Chen*, Zhongxiang Dai, Kian Hsiang Low, and Patrick Jaillet.
    NeurIPS 2019. Acceptance rate: 3% (spotlight). [Code]

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 IJCAI 2021, Program Committee Board member of IJCAI 2022-2024
  • Program Committee (PC) member/reviewer of
    • ICML (2021, 2022, 2023)
    • NeurIPS (2020, 2021, 2022)
    • ICLR (2021, 2022, 2023)
    • UAI (2023)
    • AISTATS (2023)
    • AAAI (2021, 2022, 2023)
    • CoRL (2020, 2021, 2022)
    • CVPR (2021, 2022)
    • ICCV (2021)
    • AAMAS (2023)
    • 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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