Yu-Jie Zhang @ RIKEN AIP

Conference Papers

• Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation. [arXiv]
  Long-Fei Li, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou.
  In: Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, 2023. Page to appear.
  
  
• Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. [PDF, bibtex]
  Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou.
  In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 2024. Page: 41383-41415.
  
  
• Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. [PDF, bibtex]
  Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama.
  In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. Page: 50683-50710.
  
  
• Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. [PDF, arXiv, bibtex]
  Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, and Masashi Sugiyama.
  In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 29074-29113.
  
  
• Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. [PDF, bibtex] (Spotlight)
  Yu-Jie Zhang and Masashi Sugiyama.
  In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 29741-29782.
  
  
• Imitation Learning from Vague Feedback. [PDF, bibtex]
  Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang and Masashi Sugiyama.
  In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 48275-48292.
  
  
• Adapting to Online Label Shift with Provable Guarantees. [PDF, arXiv, code, bibtex]
  Yong Bai*, Yu-Jie Zhang*, Peng Zhao, Masashi Sugiyama, and Zhi-Hua Zhou. (* indicates equal contribution)
  In: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, Louisiana, 2022. Page: 29960-29974.
  
  
• Adaptive Learning for Weakly Labeled Streams. [PDF, code, bibtex]
  Zhen-Yu Zhang, Yu-Yang Qian, Yu-Jie Zhang, Yuan Jiang, Zhi-Hua Zhou.
  In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), Washington, DC, 2022.
  
  
• Towards Enabling Learnware to Handle Unseen Jobs. [PDF, code, bibtex]
  Yu-Jie Zhang, Yu-Hu Yan, Peng Zhao and Zhi-Hua Zhou.
  In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), online, 2021. Page: 10964-10972.
  
  
• Exploratory Machine Learning with Unknown Unknowns.[PDF, code, bibtex]
  Peng Zhao, Yu-Jie Zhang and Zhi-Hua Zhou.
  In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), online, 2021. Page: 10999-11006.
  
  
• An Unbiased Risk Estimator for Learning with Augmented Classes.[PDF, arXiv, code, bibtex]
  Yu-Jie Zhang, Peng Zhao, Lanjihong Ma and Zhi-Hua Zhou.
  In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020. Page: 10247-10258.
  
  
• Dynamic Regret of Convex and Smooth Functions.[PDF, arXiv, bibtex]
  Peng Zhao, Yu-Jie Zhang, Lijun Zhang and Zhi-Hua Zhou.
  In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020. Page: 12510-12520.
  
  
• A Simple Online Algorithm for Competing with Dynamic Comparators. [PDF, bibtex, slide]
  Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou.
  In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), online, 2020.