About Me

I’m a third-year PhD student at the State Key Laboratory of Pattern Recognition, the University of Chinese Academy of Sciences, advised by Prof. Tieniu Tan. I have also spent time at Microsoft, advised by Prof. Jingdong Wang, alibaba DAMO Academy, work with Prof. Rong Jin. I strongly believe in the power of interdisciplinary collaboration and the potential it holds for driving impactful research outcomes. If you are interested in partnering on research projects, offering internship opportunities or exchange programs, I would be thrilled to connect with you.

My research interest lies in the development of robust and reliable machine learning (ML) systems that can effectively handle unexpected inputs and distribution shifts. While conventional software systems are expected to provide warnings for unexpected inputs, ML systems often fail silently due to their strong dependence on specific input properties, such as the assumption of independent and identically distributed (i.i.d.) data. In light of this challenge, my research aims to address three critical objectives.

Firstly, I seek to develop innovative techniques to detect and identify distribution shifts in ML systems. This involves exploring statistical and machine learning methods that can effectively capture changes in data distributions and trigger appropriate responses.
Secondly, I am interested in developing methods that can dynamically adapt and correct classifiers on the fly, responding to distribution shifts in real-time when possible, this involves investigating approaches such as online learning, active learning, and adaptive algorithms that can update models and decision boundaries based on evolving data distributions.
Ultimately, my work aims to contribute to developing foundational principles for building ML systems that can be relied upon in real-world scenarios, providing practical guidance for implementing robust and dependable ML systems.

🔥 News

2024.03 🎉🎉 two of our papers considering In-context-learning and symbolic reasoning have been accepted by NAACL 2024!
2023.12 🎉🎉 I will be contributing as a reviewer for IEEE TIP, International Journal of Computer Vision (IJCV), CVPR 2024, and NAACL 2024.
2023.10 🎉🎉 OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling has been accepted by NeurIPS 2023. [Code][Reading Notes]
2023.05 🎉🎉 Domain-Specific Risk Minimization for Out-of-Distribution Generalization has been accepted by SIGKDD 2023. [Code][Reading Notes]
2023.05 🎉🎉 AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation has been accepted by ICML 2023. [Code] [Reading Notes]
2023.01 🎉🎉 Free Lunch for Domain Adversarial Training: Environment Label Smoothing has been accepted by ICLR 2023. [Code] [Reading Notes]
2023.01 🎉🎉 Learning Domain Invariant Representations for Generalizable Person Re-Identification has been accepted by IEEE Transactions on Image Processing (T-IP).
2022.11 🎉🎉 Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation has been accepted by NeurIPS ML Safety workshop. [Code]
2022.04 🎉🎉 Towards Principled Disentanglement for Domain Generalization has been selected for an CVPR Oral presentation. [Reading Notes] [Code]

Publications

A Study on the Calibration of In-context Learning | NAACL, 2024 |

Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric Xing, Himabindu Lakkaraju, Sham Kakade

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling | NeurIPS, 2023 | [Code] |

Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Domain-Specific Risk Minimization for Out-of-Distribution Generalization | SIGKDD, 2023 | [Code] |

Yi-Fan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng Tao, Xing Xie

AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation | ICML, 2023 | [Code] | [Reading Notes] |

Yi-Fan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Free Lunch for Domain Adversarial Training: Environment Label Smoothing | ICLR, 2023 | [Code] | [Reading Notes] |

Yi-Fan Zhang, xue wang, Jian Liang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Towards Principled Disentanglement for Domain Generalization | CVPR 2022 Oral | Code

Hanlin Zhang*, Yi-Fan Zhang*, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation | NeurIPS ML Safety Workshop, 2022 | Code

Yi-Fan Zhang*, Hanlin Zhang*, Zachary Lipton, Li Erran Li, Eric Xing

Learning Domain Invariant Representations for Generalizable Person Re-Identification | IEEE T-IP, 2023 |

Yi-Fan Zhang; Zhang Zhang; Da Li; Zhen Jia; Liang Wang; Tieniu Tann

Focal and efficient IOU loss for accurate bounding box regression | Neurocomputing, 2022 |

Yi-Fan Zhang, Weiqiang Ren, Zhang Zhang, Zhen Jia, Liang Wang, Tieniu Tan

(* denotes equal contribution.)

Service

💻 Conference: PC Member/Reviewer for: ICML (2022,2023), NeurIPS (2022,2023), ECCV (2022), AAAI (2023,2024), CVPR (2022,2023,2024), ICCV (2023), ICLR (2023,2024), EMNLP (2023), NAACL (2024)

💻 Journal: PC Member/Reviewer for IEEE Transactions on Image Processing, International Journal of Computer Vision (IJCV)

💻 Workshops: PC Member for MILETS@PAKDD’23, DMLR@ICML’23

🎖 Selected Awards

University of Chinese Academy of Sciences 三好学生标兵，国家奖学金, 2023
Top Ten Best Student Models of South China University of Technology (Summa Cum Laude), 2020
Jingtang He Technology Innovation Scholarship (Top 1‰, 5 out of 10000+ in university), 2020
Contemporary Undergraduate Mathematical Contest in Modeling(CUMCM), National first prize (Top 1% globally), 2019.

📖 Work experience

May 2022 - Now: Research Assistant on Alibaba DAMO.
March 2021 - July: Research Assistant on Microsoft Research Asia.