Yulian Wu (伍玉莲)

Division of Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE)
King Abdullah University of Science and Technology
Thuwal, Saudi Arabia, 23955-6900
Email: yulian.wu@kaust.edu.sa

Bio

I am a Ph.D. candidate in the Center of Excellence for Generative AI (GenAI) under Computer Science at King Abdullah University of Science and Technology (KAUST). I am very fortunate to be advised by Prof.Francesco Orabona. Prior to this, I received my Master’s degree in Statistics under the supervision of Prof. Zhou Yu in 2021, and my Bachelor’s degree in Mathematics and Applied Mathematics in 2018 both at East China Normal University.

Research Interests

• Trustworthy Machine Learning: Differential Privacy, Robustness, Heavy-tail
• Interactive Decision-Making: Bandits, Reinforcement Learning
• AI Alignment: Reinforcement Learning with Human Feedback, Direct Preference Optimization
• Others: Trustworthy Issues in Bioinformatics, Quantum Machine Learning

Preprints （* indicates equal contribution）

• Offline and Online KL-Regularized RLHF under Differential Privacy.
  Yulian Wu, Rushil Thareja, Praneeth Vepakomma and Francesco Orabona.
  Accepted by NeurIPS 2025 Workshop on ResponsibleFM

• Quantum Heavy-tailed Bandits.
  Yulian Wu*, Chaowen Guan*, Vaneet Aggarwal and Di Wang

• Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning.
  Bhargav Ganguly, Yulian Wu, Di Wang and Vaneet Aggarwal

Publications （* indicates equal contribution）[Google Scholar][DBLP]

• Optimal Regret of Bernoulli Bandits under Global Differential Privacy
  Achraf Azize, Yulian Wu, Junya Honda, Francesco Orabona, Shinji Ito and Debabrota Basu.
  NeurIPS 2025

• A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO.
  Xingyu Zhou, Yulian Wu and Francesco Orabona.
  ICML 2025 (Spotlight=2.6%)

• Square𝜒PO: Differentially Private and Robust 𝜒2-Preference Optimization in Offline Direct Alignment.
  Xingyu Zhou, Yulian Wu, Wenqian Weng and Francesco Orabona.
  ICML 2025

• Better-than-KL PAC-Bayes Bounds.
  Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang and Francesco Orabona.
  The 37th Annual Conference on Learning Theory (COLT 2024)
  Youtube Video from ML New Papers

• PPML-Omics: a Privacy-Preserving federated Machine Learning system protects patients’ privacy from omic data.
  Juexiao Zhou*, Siyuan Chen*, Yulian Wu*, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Di Wang and Xin Gao.
  Science Advances (Impact Factor = 15.4), 2024
  News: Inside Precision Medicine, Today Headline, Tech Xplore, ScienMag, Bioengineer.org, Newswise, EurekAlert, Biocompare, nabd.ws

• On Private and Robust Bandits.
  Yulian Wu*, Xingyu Zhou*, Youming Tao and Di Wang.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

• Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards.
  Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury and Di Wang.
  The 40th International Conference on Machine Learning (ICML 2023).

• Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits.
  Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang.
  The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022).
  Selected as an Oral paper (Acceptance Rate: 44/1685=2.6%).
  CCS 2021 workshop on Privacy Preserving Machine Learning
  ICML 2022 workshop on Responsible Decision Making in Dynamic Environments (Oral)

• Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited.
  Youming Tao, Yulian Wu, Xiuzhen Cheng and Di Wang.
  The 31st International Joint Conference on Artificial Intelligence (IJCAI 2022).

• Performance Improvement Based on Pinball Support Vector Machine.
  Yulian Wu.
  Master Thesis, 2021

Recent Research Experience

• Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), United Arab Emirates (07/2025 - Now)
  Visiting Student
  Host: Qiang Sun (University of Toronto) in Statistics and Praneeth Vepakomma (MIT) in Machine Learning

• The University of Tokyo & RIKEN AIP & Kyoto University, Japan (11/2024-01/2025)
  Position: Student Trainee
  Host: Shinji Ito and Junya Honda

• Microsoft Research Asia, Beijing, China (07/2023- 10/2023)
  Onsite Research Intern
  Mentor: Siwei Wang

Teachings

• Teaching Assistant of CS394Z: Introduction to Online Learning, Spring 2024 @KAUST
• Teaching Assistant of CS394V: Cont. Topics in Reinforcement Learning, Fall 2022 @KAUST
• Teaching Assistant of CS229: Machine Learning, Spring 2022, Spring 2023 @KAUST
• Teaching Assistant of Statistical Software: R Language, Spring 2020 @ECNU

Selected Awards

• NeurIPS Top Reviewer, 2025
• NeurIPS Travel Award, 2023
• NIPS Grant by KAUST AI Initiative, 2022
• ICML Travel Award, 2022
• Outstanding Master’s Thesis of East China Normal University (Top 4% : 1/25), 2021

Services

• Organizing Committee Member and Moderator: KAUST Rising Stars in AI Symposium 2025, KAUST Rising Stars in AI Symposium 2026
• Program Committee member/Conference Reviewer: SIGKDD 2023, SDM 2024, AISTATS 2024, SIGKDD 2024, NeurIPS 2024, SIGKDD 2025, ICLR 2025, AISTATS 2025, ICML 2025, NeurIPS 2025 (Top Reviewer), AISTATS 2026, ICLR 2026
• Journal Reviewer: IEEE Transactions on Signal Processing

Miscellaneous

• My hobbies include scuba diving (AOW), snowboarding, and golf.