I am a last-year Ph.D. student at Department of Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Quanquan Gu. Previously, I earned my Bachelor of Science from EECS at Peking University summa cum laude, where I was very fortunate to be advised by Prof. Liwei Wang.

My research interest covers various aspects of machine learning theory, including deep learning and reinforcement learning. You can find my curriculum vitae here.

🔥 News

• 2024.01:  🎉🎉 2 papers accepted to ICLR 2024, Vienna.
• 2023.08:   It is my great honor to have been awarded the UCLA Dissertation Year Fellowship!
• 2023.07:  🎉🎉 1 paper accepted to ICML 2023, Hawaii.
• 2022.09:  🎉🎉 2 papers accepted to NeurIPS 2022, New Orlean.

📝 Publications

• Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits, ICLR 2024
  Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu

• DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text, ICLR 2024
  Xianjun Yang, Wei Cheng, Yue Wu, Linda Petzold, William Yang Wang, Haifeng Chen

• Personalized Federated Learning under Mixture of Distributions, ICML 2023
  Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng

• Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension, UAI 2023
  Yue Wu*, Jiafan He*, Quanquan Gu

• Active Ranking without Strong Stochastic Transitivity, NeurIPS 2022
  Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud

• Towards Understanding the Mixture-of-Experts Layer in Deep Learning, NeurIPS 2022
  Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li

• Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons, AISTATS 2022
  Yue Wu*, Tao Jin*, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu

• Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation, AISTATS 2022
  Yue Wu, Dongruo Zhou, Quanquan Gu,

• A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods, NeurIPS 2020
  Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu,

• Towards Understanding the Spectral Bias of Deep Learning, IJCAI 2021
  Yuan Cao*, Zhiying Fang*, Yue Wu*, Dingxuan Zhou, Quanquan Gu

• To What Extent Do Different Neural Networks Learn the Same Representation: A Neuron Activation Subspace Match Approach, NeurIPS 2019 Spotlight
  Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Liwei Wang

📖 Teaching

• 2021 Winter Teaching Assistant, UCLA CS161: Introduction to Artificial Intelligence
• 2022 Winter Teaching Assistant, UCLA CS161: Introduction to Artificial Intelligence

💬 Academic Service

• Reviewers of NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI and other conferences/journals in machine learning/data mining.
• Senior PC members of AAAI’23