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Ying WEI (魏 颖)
ying.wei [at] zju [dot] edu [dot] cn

I am currently a ZJU-100 Young Professor with College of Computer Science and Technology, Zhejiang University.

I am generally interested in developing algorithms that equip machines with more general intelligence via knowledge transfer and compositionality. This includes allowing continuous transfer and adaptation of the knowledge previous learned (nowadays in LLMs) to quickly learn the current task with minimal human supervision, and autonomously evaluating the success of knowledge transfer. I am also passionate about applying these algorithms into real-world applications with small data, e.g., AI for Science.

Previously, I was a Nanyang Assistant Professor with College of Computing and Data Science, Nanyang Technological University, an Assistant Professor at Department of Computer Science, City University of Hong Kong and a senior researcher at Tencent AI Lab. I completed my Ph.D. in Computer Science and Engineering at Hong Kong University of Science and Technology under the supervision of Professor Qiang Yang, and my B.S. in Automation at Huazhong University of Science and Technology. I have also spent time interning at Microsoft Research Asia.

I am looking for highly-motivated full-time PhD students, post-doctoral research fellows, and also research assistants!

PhD Thesis  /  LinkedIn  /  Google Scholar

News
  • [Sep 2024] Our paper "Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models" was accepted by NeurIPS 2024!
  • [Sep 2024] Our paper "Learning Where to Edit Vision Transformers" was accepted by NeurIPS 2024!
  • [Sep 2024] Our paper "Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-tuning" was accepted by NeurIPS 2024!
  • [Sep 2024] Our paper "DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs" was accepted by NeurIPS 2024!
  • [Sep 2024] Our paper "Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing" was accepted by EMNLP 2024!
  • [Sep 2024] Serving as an Area Chair for ICLR 2025
  • [July 2024] We are organizing the NeurIPS 2024 Workshop on Compositional Learning! For more information, please visit our website.
  • [July 2024] We are organizing the NeurIPS 2024 Workshop on Advancements In Medical Foundation Models! For more information, please visit our website.
  • [May 2024] Our paper "Understanding and Patching Compositional Reasoning in LLMs" was accepted by ACL 2024 Findings!
  • [May 2024] Our paper "Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generatio" was accepted by ACL 2024!
  • [May 2024] Honored to receive the ICLR 2024 Outstanding Honorable Mention!
  • [May 2024] Our paper "Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts" was accepted by ICML 2024!
  • [May 2024] Our paper "One Meta-tuned Transformer is What You Need for Few-shot Learning" was accepted by ICML 2024!
  • [May 2024] Our paper "Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations" was accepted by ICML 2024!
  • [May 2024] Our paper "Federated Continual Learning via Prompt-based Dual Knowledge Transfer" was accepted by ICML 2024!
  • [Apr 2024] Serving as an Area Chair for NeurIPS 2024
  • [Feb 2024] Our paper "MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric" was accepted by CVPR 2024!
  • [Jan 2024] Our paper "Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction" was accepted by ICLR 2024 (oral)!
  • [Jan 2024] Our paper "Gradual Domain Adaptation via Gradient Flow" was accepted by ICLR 2024 (spotlight)!
  • [Jan 2024] Our paper "Active Retrosynthetic Planning Aware of Route Quality" was accepted by ICLR 2024!
  • [Jan 2024] Serving as an Area Chair for ICML 2024
  • [Jan 2024] Serving as an Action Editor for Transactions on Machine Learning Research (TMLR)
  • [Jan 2024] Our paper "RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction" was accepted by AAAI 2024!
  • [Sep 2023] Our paper "Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts" was accepted by EMNLP 2023
  • [Sep 2023] Our paper "Secure Out-of-Distribution Task Generalization with Energy-Based Models" was accepted by NeurIPS 2023
  • [Sep 2023] Our paper "Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework" was accepted by NeurIPS 2023
  • [Sep 2023] I joined Nanyang Technological University as a Nanyang Assistant Professor
  • [July 2023] Our paper "Concept-wise Fine-tuning Matters in Preventing Negative Transfer" was accepted by ICCV 2023
  • [Apr 2023] Our paper "Learning to Substitute Spans towards Improving Compositional Generalization" was accepted by ACL 2023
Publications


( ___: equal contribution, *: corresponding author)

Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models
Zhan Zhuang, Yulong Zhang, Xuehao Wang, Jiangang Lu, Ying Wei*, Yu Zhang*
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
pdf / code coming soon

Learning Where to Edit Vision Transformers
Yunqiao Yang, Long-Kai Huang, Shengzhuang Chen, Kede Ma, Ying Wei*
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
pdf / code coming soon

DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Yingtao Zhang, Linzhan Mou, Linqi Song, Zhenan Sun*, Ying Wei*
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
pdf / code (oral)

Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-tuning
Xu Yang, Chen Liu, Ying Wei*
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
pdf / code coming soon

Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing
Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song*, Ying Wei*
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
pdf / code coming soon

Understanding and Patching Compositional Reasoning in LLMs
Zhaoyi Li, Gangwei Jiang, Hong Xie, Linqi Song, Defu Lian*, Ying Wei*
Sixty-second Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024
pdf / code

Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation
Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying Wei, Defu Lian, Zhendong Mao*.
Sixty-second Annual Meeting of the Association for Computational Linguistics (ACL), 2024
pdf / code

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei*
Forty-first International Conference on Machine Learning (ICML), 2024
pdf / code

One Meta-tuned Transformer is What You Need for Few-shot Learning
Xu Yang, Huaxiu Yao, Ying Wei*
Forty-first International Conference on Machine Learning (ICML), 2024
pdf / code (spotlight)

Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations
Yichen Wu, Hong Wang, Peilin Zhao, Yefeng Zheng, Ying Wei*, Long-Kai Huang*
Forty-first International Conference on Machine Learning (ICML), 2024
pdf / code

Federated Continual Learning via Prompt-based Dual Knowledge Transfer
Hongming Piao, Yichen Wu, Dapeng Wu, Ying Wei*
Forty-first International Conference on Machine Learning (ICML), 2024
pdf / code

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
Haokun Lin, Haoli Bai, Zhili Liu, Lu Hou, Muyi Sun, Linqi Song, Ying Wei*, Zhenan Sun*
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024
pdf / code

Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction
Yichen Wu, Long-Kai Huang, Renzhen Wang, Deyu Meng, Ying Wei*
Twelfth International Conference on Learning Representations (ICLR), 2024 (Outstanding Honorable Mention / oral)
pdf / code

Gradual Domain Adaptation via Gradient Flow
Zhan Zhuang, Yu Zhang*, Ying Wei*
Twelfth International Conference on Learning Representations (ICLR), 2024 (spotlight)
pdf / code

Active Retrosynthetic Planning Aware of Route Quality
Luotian Yuan, Yemin Yu, Ying Wei*, Yongwei Wang, Zhihua Wang, Fei Wu*
Twelfth International Conference on Learning Representations (ICLR), 2024
pdf / code

RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Yemin Yu, Luotian Yuan, Ying Wei*, Hanyu Gao, Xinhai Ye, Zhihua Wang, Fei Wu
Thirty-eighth Annual AAAI Conference on Artificial Intelligence (AAAI), 2024
pdf / code

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts
Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Y. Zhang, Jun Zhou, Defu Lian*, Ying Wei*
2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
pdf

Secure Out-of-Distribution Task Generalization with Energy-Based Models
Shengzhuang Chen, Long-Kai Huang, Jonathan Richard Schwarz, Yilun Du, Ying Wei*
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
pdf / code

Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework
Weiduo Liao, Ying Wei*, Mingchen Jiang, Qingfu Zhang*, Hisao Ishibuchi*
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
Track on Datasets and Benchmarks
pdf / code

Concept-wise Fine-tuning Matters in Preventing Negative Transfer
Yunqiao Yang, Long-Kai Huang, Ying Wei*
IEEE/CVF International Conference on Computer Vision (ICCV), 2023
pdf / code

Learning to Substitute Spans towards Improving Compositional Generalization
Zhaoyi Li, Ying Wei*, Defu Lian*
Sixty-first Annual Meeting of the Association for Computational Linguistics (ACL), 2023 (oral)
pdf / code

Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Weixia Zhang, Guangtao Zhai, Ying Wei, Xiaokang Yang, Kede Ma
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
pdf

Learning Chemical Rules of Retrosynthesis with Pre-training
Yinjie Jiang, Ying Wei*, Fei Wu*, Zhengxing Huang, Kun Kuang, Zhihua Wang
Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023
pdf

Adversarial Task Up-sampling for Meta-learning
Yichen Wu, Long-Kai Huang*, Ying Wei*
36th Conference on Neural Information Processing Systems (NeurIPS), 2022 (spotlight)
pdf / code

Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Long-Kai Huang, Ying Wei*
36th Conference on Neural Information Processing Systems (NeurIPS), 2022 (spotlight)
pdf

GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy
Yemin Yu, Ying Wei*, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu*
36th Conference on Neural Information Processing Systems (NeurIPS), 2022
pdf / code

Frustratingly Easy Transferability Estimation
Long-Kai Huang, Junzhou Huang, Qiang Yang, Ying Wei*
39th International Conference on Machine Learning (ICML), 2022
pdf / code

The Role of Deconfounding in Meta-learning
Yinjie Jiang, Zhengyu Chen, Kun Kuang*, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu*, Ying Wei*
39th International Conference on Machine Learning (ICML), 2022
pdf

Artificial Intelligence for Retrosynthesis Prediction
Yinjie Jiang, Yemin Yu, Ming Kong, Yu Mei, Luotian Yuan, Zhengxing Huang, Kun Kuang, Zhihua Wang, Huaxiu Yao, James Zou, Connor W. Coley, Ying Wei*
Engineering, 2022
pdf

Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering
Juan Zha, Zheng Li, Ying Wei, Yu Zhang
2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
pdf

Self-supervised Text Erasing with Controllable Image Synthesis
Gangwei Jiang, Shiyao Wang, Tiezheng Ge, Yuning Jiang, Ying Wei, Defu Lian
30th ACM International Conference on Multimedia (MM), 2022
pdf

Meta-learning with an Adaptive Task Scheduler
Huaxiu Yao, Yu Wang, Ying Wei*, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn
35th Conference on Neural Information Processing Systems (NeurIPS), 2021
pdf / code

Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
Huaxiu Yao, Ying Wei*, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui Li
35th Conference on Neural Information Processing Systems (NeurIPS), 2021
pdf

MetaTS: Meta Teacher-Student Network for Multilingual SequenceLabeling with Minimal Supervision
Zheng Li, Danqing Zhang, Tianyu Cao, Ying Wei, Yiwei Song, Bing Yin
2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
pdf

Meta-learning Hyperparameter Performance Prediction with Neural Processes
Ying Wei, Peilin Zhao, Junzhou Huang
38th International Conference on Machine Learning (ICML), 2021
pdf / code

Improving Generalization in Meta-learning via Task Augmentation
Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei*, Li Tian, James Zou, Junzhou Huang, Zhenhui Li
38th International Conference on Machine Learning (ICML), 2021
pdf / arXiv / code

Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages
Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying Wei, Yu Zhang, Qiang Yang
2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
pdf

Self-Supervised Graph Transformer on Large-Scale Molecular Data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang
34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
pdf

Adversarial Sparse Transformer for Time Series Forecasting
Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang
34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
pdf

Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Qingyao Wu, Peilin Zhao, Shuaicheng Niu, Mingkui Tan, Junzhou Huang
IEEE Transactions on Image Processing, 2020
pdf

TranSlider: Transfer Ensemble Learning from Exploitation to Exploration
Kuo Zhong, Ying Wei*, Chun Yuan, Haoli Bai, Junzhou Huang
Twenty-sixth ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
pdf

Graph Few-shot Learning via Knowledge Transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei*, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, Zhenhui Li
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020
pdf


Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, Qiang Yang
2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
pdf / code

From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification
Yifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao, Qingyao Wu, Mingkui Tan, Junzhou Huang
22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
pdf

Hierarchically Structured Meta-learning
Huaxiu Yao, Ying Wei*, Junzhou Huang, Zhenhui Li
36th International Conference on Machine Learning (ICML), 2019
pdf / code


Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification
Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, Xin Li
33rd AAAI Conference on Artificial Intelligence (AAAI) , 2019
pdf / code

Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang
32nd Annual Conference on Neural Information Processing Systems (NeurIPS), 2018
pdf

Transfer learning via Learning to Transfer
Ying Wei, Yu Zhang, Junzhou Huang, Qiang Yang
35th International Conference on Machine Learning (ICML), 2018 (long talk)
pdf

Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification
Zheng Li, Ying Wei, Yu Zhang, Qiang Yang
32nd AAAI Conference on Artificial Intelligence (AAAI), 2018
pdf / code

Transferable Contextual Bandit for Cross-Domain Recommendation
Bo Liu, Ying Wei, Yu Zhang, Qiang Yang
32nd AAAI Conference on Artificial Intelligence (AAAI), 2018
pdf


End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification
Zheng Li, Yu Zhang, Ying Wei, Qiang Yang
26th International Joint Conference on Artificial Intelligence (IJCAI), 2017
pdf / code

Deep Neural Networks for High Dimension, Low Sample Size Data
Bo Liu, Ying Wei, Yu Zhang, Qiang Yang
26th International Joint Conference on Artificial Intelligence (IJCAI), 2017
pdf

Heterogeneous Translated Hashing: A Scalable Solution towards Multi-modal Similarity Search
Ying Wei, Yangqiu Song, Yi Zhen, Bo Liu, Qiang Yang
ACM Transactions on Knowledge Discovery from Data (TKDD), 10(4):36, 2016
pdf

Transfer Knowledge between Cities
Ying Wei, Yu Zheng, Qiang Yang
22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
pdf

Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning
Ying Wei, Yin Zhu, Cane Wing-ki Leung Yangqiu Song, Qiang Yang
30th AAAI Conference on Artificial Intelligence (AAAI), 2016
pdf

Scalable Heterogeneous Translated Hashing
Ying Wei, Yangqiu Song, Yi Zhen, Bo Liu, Qiang Yang
20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014
Best Paper Finalist
pdf



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