Research

The long-term research goal is to build robust models for modern AI, such as pre-trained models and large models. We create new theory, algorithms, applications, and open-sourced library to achieve our goal. These days, we are specifically interested in robustness in large language models (LLMs).
Our research consists of the following topics with selected publications: [View by year]
Out-of-distribution (Domain) generalization and adaptation for distribution shift
- [ICLRâ23] Out-of-distribution Representation Learning for Time Series Classification. Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie.
- [KDDâ23] Domain-Specific Risk Minimization for Out-of-Distribution Generalization. YiFan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Xing Xie, and Dacheng Tao.
- [KDDâ23] Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning. Xin Qin, Jindong Wang, Shuo Ma, Wang Lu, Yongchun Zhu, Xing Xie, Yiqiang Chen.
- [ACLâ23 findings] GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective. Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang.
- [TKDEâ22] Generalizing to Unseen Domains: A Survey on Domain Generalization. Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S. Yu.
- [TMLRâ22] Domain-invariant Feature Exploration for Domain Generalization. Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen, and Xing Xie.
- [UbiCompâ22] Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition. Wang Lu, Jindong Wang, Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin.
- [NeurIPSâ21] Learning causal semantic representation for out-of-distribution prediction. Chang Liu, Xinwei Sun, Jindong Wang , Haoyue Tang, Tao Li, Tao Qin, Wei Chen, and Tie-Yan Liu.
- [CIKMâ21] Adarnn: Adaptive learning and forecasting of time series. Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang.
- [TNNLSâ20, 300 + citations] Deep subdomain adaptation network for image classification. Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, and Qing He.
- [ACMMMâ18, 400+ citations] Visual domain adaptation with manifold embedded distribution alignment. Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S Yu.
- [ICDMâ17, 400+ citations] Balanced distribution adaptation for transfer learning. Jindong Wang, Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen.
- Open-source:
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Transfer learning
- robustlearn: A unified repo for robust machine learning, such as OOD and adversarial robustness: robustlearn
- PandaLM: PandaLM
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Transfer learning
Semi-supervised learning for low-resource learning
- [ICLRâ23] FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning. Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, and Xing Xie.
- [ICLRâ23] SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning. Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, and Marios Savvides.
- [NeurIPSâ22] USB: A Unified Semi-supervised Learning Benchmark. Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, and Yue Zhang.
- [TASLPâ22] Exploiting Adapters for Cross-lingual Low-resource Speech Recognition. Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, and Takahiro Shinozaki.
- [NeurIPSâ21, 200+ citations] Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki.
- Open-source:
Safe transfer learning for security
- [arXivâ23] On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective. Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, and Xing Xie.
- [ICSEâ22] ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing. Ziqi Zhang, Yuanchun Li, Jindong Wang, Bingyan Liu, Ding Li, Xiangqun Chen, Yao Guo, and Yunxin Liu.
- [IEEE TBDâ22] Personalized Federated Learning with Adaptive Batchnorm for Healthcare. Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, and Tao Qin.
- [TKDEâ22] Unsupervised deep anomaly detection for multi-sensor time-series signals. Yuxin Zhang, Yiqiang Chen, Jindong Wang, and Zhiwen Pan.
- [IntSysâ22, 400+ citations] Fedhealth: A federated transfer learning framework for wearable healthcare. Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao.
- Open-source:
- PersonalizedFL: a personalized federated learning libraty: PersonalizedFL
- robustlearn: A unified repo for robust machine learning, such as OOD and adversarial robustness: robustlearn
- PersonalizedFL: a personalized federated learning libraty: PersonalizedFL
Imbalanced learning for long-tailed tasks
- [arXivâ23] Exploring Vision-Language Models for Imbalanced Learning. Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang.
- [ACMLâ22] Margin Calibration for Long-Tailed Visual Recognition. Yidong Wang, Bowen Zhang, Wenxin Hou, Zhen Wu, Jindong Wang, and Takahiro Shinozaki.
- Open-source:
- Imbalance-VLM: a library for imbalanced learning in vision-language models. [Imbalance-VLM]
Miscellaneous
- An easy-to-use speech recognition toolkit based on Espnet: EasyESPNet
- Leading the transfer learning tutorial (èżç§»ćŠäč çźææć) on Github: Tutorial
- Iâm also leading other popular research projects: Machine learning, Activity recognition
- I started a software studio Pivot Studio and made many applications in 2010-2014:
Our applications