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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• 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!

• National University of Singapore (NUS)   (Aug 2017 - Apr 2021)
  • Ph.D. student in Artificial Intelligence, Department of Computer Science
  • Advisors: Bryan Kian Hsiang Low (NUS) & Patrick Jaillet (MIT)
  • Thesis: Sample-Efficient Automated Machine Learning with Bayesian Optimization
  • Supported by Singapore-MIT Alliance for Research and Technology (SMART) Graduate Fellowship, eligible for co-supervision by an MIT faculty and research residency at MIT for up to six months
• National University of Singapore (NUS)   (Aug 2011 - Jun 2015)
  • Bachelor of Engineering (Electrical Engineering), First Class Honors

• Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization.
  Shouri Hu, Haowei Wang, Zhongxiang Dai, Kian Hsiang Low and Szu Hui Ng.
  Pre-print, 2022 [arXiv]
  

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]
    

• 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

• 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)

• 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).

• 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)