Publications | Jindong Wang

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Summary: Nature (1), Nature portfolio journal (1), ICLR (14), NeurIPS (13), ICML (12), KDD (3), IEEE TPAMI (2), IJCAI (3), CVPR (4), IEEE TKDE (4), ACL (8), AAAI (1), ICCV (1), ECCV (1), UbiComp (3), MM (1).

Seleted Publications

[Nature] General scales unlock ai evaluation with explanatory and predictive power.
[NeurIPS’21] Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. (1400+ Citations; Top 17 most cited NeurIPS papers in the past 5 years)
[ICML’24 Oral (Top 3%)] CompeteAI: Understanding Competition Dynamics of LLM-based Agents.
[NeurIPS’24 Spotlight (Top 5%)] Slight Corruption in Pre-training Data Makes Better Diffusion Models.
[IEEE TPAMI’25] Impact of Noisy Supervision in Foundation Model Learning.
[ICLR’24 Spotlight (Top 5%)] DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks.
[ICLR’24 Spotlight (Top 5%)] Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks.
[ICML’24] The good, the bad, and why: Unveiling emotions in generative ai. (Reported by Forbes and many international media)
[IEEE TPAMI’24] DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization.
[CIKM’21] Adarnn: Adaptive learning and forecasting of time series. (Paperdigest most influencial paper)
[ACM MM’18 Oral] Visual domain adaptation with manifold embedded distribution alignment. (800 Citations; 2nd most cited paper in MM’18)
[ICDM’17] Balanced distribution adaptation for transfer learning. (700 Citations; most cited paper in ICDM’17)

Recent Preprints

AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent. Yinyi Luo, Yiqiao Jin, Weichen Yu, Mengqi Zhang, Srijan Kumar, Xiaoxiao Li, Weijie Xu, Xin Chen, Jindong Wang. [arxiv] [code]
Thinking Makes LLM Agents Introverted: How Mandatory Thinking Can Backfire in User-Engaged Agents. Jiatong Li, Changdae Oh, Hyeong Kyu Choi, Jindong Wang, Sharon Li. [arxiv] [code]
Evolving Collective Cognition in Human-Agent Hybrid Societies: How Agents Form Stances and Boundaries. Hanzhong Zhang, Muhua Huang, Jindong Wang. [arxiv]
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems. Jiaxi Yang, Mengqi Zhang, Yiqiao Jin, Hao Chen, Qingsong Wen, Lu Lin, Yi He, Weijie Xu, James Evans, Jindong Wang. [arxiv]
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation. Chika Maduabuchi, Hao Chen, Yujin Han, Jindong Wang. [arxiv] [code]
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks. Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang. [arxiv]
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders. Cheng Li, May Fung, Qingyun Wang, Chi Han, Manling Li, Jindong Wang, Heng Ji. [arxiv]
On the Diversity of Synthetic Data and its Impact on Training Large Language Models. Hao Chen, Abdul Waheed, Xiang Li, Yidong Wang, Jindong Wang, Bhiksha Raj, Marah I. Abdin. [arxiv]

Books

CHAPTER 3: Improving Large Foundation Models in Education for Multi-cultural Understanding

Jindong Wang , and Shudong Liu

Future of Learning with Large Language Models: Applications and Research in Education 2025 | [ HTML ]

Introduction to Transfer Learning

Jindong Wang , and Yiqiang Chen

Spriner Nature 2023 | [ HTML Website Zhihu ]
Activity Recognition

Jindong Wang , Yiqiang Chen, and Chunyu Hu

Springer International Publishing 2023 | [ HTML ]

All Papers

2026

General scales unlock ai evaluation with explanatory and predictive power

Lexin Zhou, Lorenzo Pacchiardi, Fernando Martı́nez-Plumed, Katherine M Collins, Yael Moros-Daval, Seraphina Zhang, Qinlin Zhao, Yitian Huang, Luning Sun, Jonathan E Prunty, Zongqian Li, Pablo Sánchez-Garcı́a, Kexin Jiang Chen, Pablo A. M. Casares, Jiyun Zu, John Burden, Behzad Mehrbakhsh, David Stillwell, Manuel Cebrian, Jindong Wang , Peter Henderson, Sherry Tongshuang Wu, Patrick C. Kyllonen, Xing Xie, and José Hernández-Orallo

Nature (accepted) 2026 | [ arXiv Code ]
Classroom AI: Large Language Models as Grade-Specific Teachers

Jio Oh, Steven Euijong Whang, James Evans, and Jindong Wang^#

NPJ Artificial Intelligence (Nature Portfolio Journal) 2026 | [ arXiv ]
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary

Zhaolong Su, Wang Lu, Hao Chen, Sharon Li, and Jindong Wang^#

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 | [ arXiv Code ]
Self-Corrected Image Generation with Explainable Latent Rewards

Yinyi Luo, Hrishikesh Gokhale, Marios Savvides, Jindong Wang , and Shengfeng He

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 | [ ]
KnowledgeSmith: Uncovering Knowledge Updating in LLMs with Model Editing and Unlearning

Yinyi Luo, Zhexian Zhou, Hao Chen, Kai Qiu, Marios Savvides, Sharon Li, and Jindong Wang^#

International Conference on Learning Representation (ICLR) 2026 | [ arXiv Code ]
SparseEval: Efficient Evaluation of Large Language Models by Sparse Optimization

Taolin Zhang, Hang Guo, Wang Lu, Tao Dai, Shu-Tao Xia, and Jindong Wang^#

International Conference on Learning Representation (ICLR) 2026 | [ arXiv Code ]
Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models

Soyeon Kim, Jindong Wang , Xing Xie, and Steven Euijong Whang

International Conference on Learning Representation (ICLR) 2026 | [ arXiv Code ]
HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition

Wang Lu, Yao Zhu, and Jindong Wang^#

The 32rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2026 | [ arXiv Code ]
Is your (reasoning) Multimodal Language Model vulnerable toward Distractions?

Ming Liu, Hao Chen, Jindong Wang , Jingchen Sun, Liwen Wang, and Wensheng Zhang

The Fortieth AAAI Conference on Artificial Intelligence (AAAI) 2026 | [ arXiv ]
FedUMM: A General Framework for Federated Learning with Unified Multimodal Models

Zhaolong Su, Leheng Zhao, Xiaoying Wu, Ziyue Xu, and Jindong Wang^#

The Web Conference (WWW) workshop on Federated Learning 2026 | [ arXiv ]
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation

Chika Maduabuchi, Hao Chen, Yujin Han, and Jindong Wang^#

ICLR 2026 workshop on Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models (REALM-GEN) 2026 | [ arXiv Code ]

2025

Impact of Noisy Supervision in Foundation Model Learning

Hao Chen, Zihan Wang, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj, and Jindong Wang^#

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2025 | [ arXiv Code ]
Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach

Yuchen Wu, Edward Sun, Kaijie Zhu, Jianxun Lian, Jose Hernandez-Orallo, Aylin Caliskan, and Jindong Wang^#

Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025 | [ arXiv Code Website ]
On fairness of unified multimodal large language model for image generation

Ming Liu, Hao Chen, Jindong Wang , Liwen Wang, Bhiksha Raj Ramakrishnan, and Wensheng Zhang

Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025 | [ arXiv ]
From Pretraining to Pathology: How Noise Leads to Catastrophic Inheritance in Medical Models

Hao Sun, Zhongyi Han, Hao Chen, Jindong Wang , Xin Gao, and Yilong Yin

Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025 | [ Website ]
Exposing and Patching the Flaws of Large Language Models in Social Character Simulation

Yue Huang, Zhengqing Yuan, Yujun Zhou, Xiangqi Wang, Kehan Guo, Haomin Zhuang, Weixiang Sun, Lichao Sun, Jindong Wang , Yanfang Ye, and others

Conference on Language Modeling (COLM) 2025 | [ arXiv ]
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation

Mingxuan Xia, Haobo Wang, Yixuan Li, Zewei Yu, Jindong Wang , Junbo Zhao, and Runze Wu

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) 2025 | [ arXiv Code ]
Disentangling logic: The role of context in large language model reasoning capabilities

Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Shuhang Lin, Mingyu Jin, Haochen Xue, Zelong Li, JinDong Wang, and Yongfeng Zhang

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) Findings 2025 | [ arXiv Code ]
Value compass leaderboard: A platform for fundamental and validated evaluation of llms values

Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang , Yuzhuo Bai, Muhua Huang, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, and others

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) Demo Track 2025 | [ arXiv Code ]
Masked Autoencoders Are Effective Tokenizers for Diffusion Models

Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang , Ze Wang, Zicheng Liu, Difan Zou, and Bhiksha Raj

International Conference on Machine Learning (ICML) 2025 | [ arXiv Code ]

(Spotlight (Top 2.6%))
MELON: Indirect Prompt Injection Defense via Masked Re-execution and Tool Comparison

Kaijie Zhu, Xianjun Yang, Jindong Wang , Wenbo Guo, and William Yang Wang

International Conference on Machine Learning (ICML) 2025 | [ arXiv ]
Outcome-Refining Process Supervision for Code Generation

Zhuohao Yu, Weizheng Gu, Yidong Wang, Zhengran Zeng, Jindong Wang , Wei Ye, and Shikun Zhang

International Conference on Machine Learning (ICML) 2025 | [ arXiv Code ]
Topology-aware Neural Flux Prediction Guided by Physics

Haoyang Jiang, Jindong Wang , Xingquan Zhu, and Yi He

International Conference on Machine Learning (ICML) 2025 | [ arXiv ]
SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

Hao Chen, Ze Wang, Xiang Li, Ximeng Sun, Fangyi Chen, Jiang Liu, Jindong Wang , Bhiksha Raj, Zicheng Liu, and Emad Barsoum

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 | [ arXiv ]
StringLLM: Understanding the String Processing Capability of Large Language Models

Xilong Wang, Hao Fu, Jindong Wang , and Neil Zhenqiang Gong

International Conference on Learning Representation (ICLR) 2025 | [ arXiv Code Website ]
Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist

Zihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu, Jindong Wang , Derek F Wong, Xiaowei Huang, Qiufeng Wang, and Kaizhu Huang

International Conference on Learning Representation (ICLR) 2025 | [ arXiv Website ]
Cycleresearcher: Improving Automated Research via Automated Review

Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang , Yue Zhang, and Linyi Yang

International Conference on Learning Representation (ICLR) 2025 | [ arXiv Website ]
PLENCH: Realistic Evaluation of Deep Partial-Label Learning Algorithms

Wei Wang, Dong-Dong Wu, Jindong Wang , Gang Niu, Min-Ling Zhang, and Masashi Sugiyama

International Conference on Learning Representation (ICLR) 2025 | [ arXiv Website ]

(Spotlight (Top 5.1%))
Scito2M: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis

Yiqiao Jin, Yijia Xiao, Yiyang Wang, and Jindong Wang^#

AAAI 2025 Preparing Good Data for Generative AI Workshop 2025 | [ arXiv Code ]

(Best Paper Award)
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries

Shudong Liu, Yiqiao Jin, Cheng Li, Derek F Wong, Qingsong Wen, Lichao Sun, Haipeng Chen, Xing Xie, and Jindong Wang^#

CVPR 2025 workshop on VLMs4ALL 2025 | [ arXiv ]

2024

Slight Corruption in Pre-training Data Makes Better Diffusion Models

Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang^# , and Bhiksha Raj

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code Zhihu ]

(Spotlight)
CulturePark: Boosting Cross-cultural Understanding in Large Language Models

Cheng Li, Damien Teney, Linyi Yang, Qingsong Wen, Xing Xie, and Jindong Wang^#

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code ]
Culturellm: Incorporating cultural differences into large language models

Cheng Li, Mengzhou Chen, Jindong Wang^# , Sunayana Sitaram, and Xing Xie

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code ]
Imprecise label learning: A unified framework for learning with various imprecise label configurations

Hao Chen, Ankit Shah, Jindong Wang , Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, and Bhiksha Raj

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code ]
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models

Lai Wei, Zhiquan Tan, Chenghai Li, Jindong Wang , and Weiran Huang

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code ]
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models

Jio Oh, Soyeon Kim, Junseok Seo, Jindong Wang , Ruochen Xu, Xing Xie, and Steven Euijong Whang

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code ]

(Spotlight)
AgentReview: Exploring Peer Review Dynamics with LLM Agents

Yiqiao Jin, Qinlin Zhao, Yiyang Wang, Hao Chen, Kaijie Zhu, Yijia Xiao, and Jindong Wang^#

The Conference on Empirical Methods in Natural Language Processing (EMNLP): Main Track 2024 | [ arXiv Code Website Zhihu ]

(Oral)
RESTful-Llama: Connecting User Queries to RESTful APIs

Han Xu, Ruining Zhao, Jindong Wang , and Haipeng Chen

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP): Industry Track 2024 | [ PDF ]
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models

Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang , Yue Zhang, and Shikun Zhang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP): Demo Track 2024 | [ arXiv Code ]
On catastrophic inheritance of large foundation models

Hao Chen, Bhiksha Raj, Xing Xie, and Jindong Wang^#

Data-centric Machine Learning Research (DMLR) 2024 | [ arXiv ]
Promptbench: A unified library for evaluation of large language models

Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang^# , and Xing Xie

Journal of Machine Learning Research (JMLR) 2024 | [ arXiv Code ]

(Featured by Hugging Face; Microsoft’s first LLM evaluation framework)
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization

Xixu Hu, Runkai Zheng, Jindong Wang^# , Cheuk Hang Leung, Qi Wu, and Xing Xie

ECCV 2024 | [ arXiv ]
Competeai: Understanding the competition behaviors in large language model-based agents

Qinlin Zhao, Jindong Wang^# , Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]

(Oral)
The good, the bad, and why: Unveiling emotions in generative ai

Cheng Li, Jindong Wang^# , Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]
DyVal 2: Dynamic Evaluation of Large Language Models by Meta Probing Agents

Kaijie Zhu, Jindong Wang^# , Qinlin Zhao, Ruochen Xu, and Xing Xie

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]
A General Framework for Learning from Weak Supervision

Hao Chen, Jindong Wang , Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, and Bhiksha Raj

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]
Selective mixup helps with distribution shifts, but not (only) because of mixup

Damien Teney, Jindong Wang , and Ehsan Abbasnejad

International Conference on Machine Learning (ICML) 2024 | [ arXiv ]
Open-Vocabulary Calibration for Vision-Language Models

Shuoyuan Wang, Jindong Wang , Guoqing Wang, Bob Zhang, Kaiyang Zhou, and Hongxin Wei

International Conference on Machine Learning (ICML) 2024 | [ arXiv ]
Trustllm: Trustworthiness in large language models

Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, and others

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]

(700+ citations; Featured by Hugging Face)
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang , Shirui Pan, and Qingsong Wen

International Conference on Machine Learning (ICML) 2024 | [ arXiv ]
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models

Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang , Xing Xie, Yue Zhang, and Shikun Zhang

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL) 2024 | [ arXiv Code ]
Detoxifying Large Language Models via Knowledge Editing

Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang , and Huajun Chen

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL) 2024 | [ arXiv Code ]
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms

Yiqiao Jin, Minje Choi, Gaurav Verma, Jindong Wang , and Srijan Kumar

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL) Findings 2024 | [ arXiv ]
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Haoxin Liu, Zhiyuan Zhao, Jindong Wang , Harshavardhan Kamarthi, and B Aditya Prakash

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL) Findings 2024 | [ arXiv ]
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli

Xu Wang, Cheng Li, Yi Chang, Jindong Wang , and Yuan Wu

International Joint Conference on Artificial Intelligence (IJCAI) 2024 | [ arXiv Code ]
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization

Wang Lu, Jindong Wang^# , Xinwei Sun, Yiqiang Chen, Xiangyang Ji, Qiang Yang, and Xing Xie

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2024 | [ arXiv Code Zhihu ]
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Kaijie Zhu, Jiaao Chen, Jindong Wang^# , Neil Zhenqiang Gong, Diyi Yang, and Xing Xie

International Conference on Learning Representation (ICLR) 2024 | [ arXiv Code ]

(Spotlight (Top 5%))
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks

Hao Chen, Jindong Wang^# , Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, and Bhiksha Raj

International Conference on Learning Representation (ICLR) 2024 | [ arXiv Code Zhihu ]

(Spotlight (Top 5%))
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang , Xing Xie, and others

International Conference on Learning Representation (ICLR) 2024 | [ arXiv Code Zhihu ]

(Featured by Hugging Face)
Supervised Knowledge Makes Large Language Models Better In-context Learners

Linyi Yang, Shuibai Zhang, Zhuohao Yu, Guangsheng Bao, Yidong Wang, Jindong Wang , Ruochen Xu, Wei Ye, Xing Xie, Weizhu Chen, and others

International Conference on Learning Representation (ICLR) 2024 | [ arXiv ]
A survey on evaluation of large language models

Yupeng Chang, Xu Wang, Jindong Wang^# , Yuan Wu, Kaijie Zhu, Hao Chen, Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, and others

ACM Transactions on Intelligent Systems and Technology (TIST) 2024 | [ arXiv Code Website Zhihu ]

(Featured by Hugging Face)
Generating Virtual Reality Interaction Data from Out-of-Distribution Desktop Data: An Exploration Using Stroke Gestures

Linping Yuan, Boyu Li, Jindong Wang , Huaming Qu, and Wei Zeng

The IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR) 2024 | [ HTML PDF Code Video ]
FIXED: Frustratingly Easy Domain Generalization with Mixup

Wang Lu, Jindong Wang^# , Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, and Xing Xie

Conference on Parsimony and Learning (CPAL) 2024 | [ arXiv Code ]
UP-Net: An Uncertainty-Driven Prototypical Network for Few-Shot Fault Diagnosis

Ge Yu, Jindong Wang , Jinhai Liu, Xi Zhang, Yiqiang Chen, and Xing Xie

IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2024 | [ ]
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution

Wang Lu, Jindong Wang^# , Yidong Wang, and Xing Xie

SIAM Conference on Data Mining (SDM) 2024 | [ arXiv ]
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

Shuoyuan Wang, Jindong Wang , Huajun Xi, Bob Zhang, Lei Zhang, and Hongxin Wei

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 2024 | [ arXiv Code ]
ZooPFL: Exploring black-box foundation models for personalized federated learning

Wang Lu, Hao Yu, Jindong Wang^# , Damien Teney, Haohan Wang, Yiqiang Chen, Qiang Yang, Xing Xie, and Xiangyang Ji

NeurIPS 2024 workshop on Federated Foundation Model 2024 | [ arXiv Code ]

(Outstanding Paper Award)
Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts

Kaijie Zhu, Jindong Wang^# , Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, and others

ACM Conference on Computer and Communications Security (CCS) workshop on privacy and security (Lamp) 2024 | [ arXiv Code ]

(500+ citations)
Conv-adapter: Exploring parameter efficient transfer learning for convnets

Hao Chen, Ran Tao, Han Zhang, Yidong Wang, Wei Ye, Jindong Wang , Guosheng Hu, and Marios Savvides

CVPR 2024 Workshop Prompting in Vision 2024 | [ arXiv ]

2023

Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

Andy Zhou, Jindong Wang , Yu-Xiong Wang, and Haohan Wang

Advances in Neural Information Processing Systems (NeurIPS) 2023 | [ arXiv Code ]
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future

Linyi Yang, Yaoxiao Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Lingqiao Liu, Jindong Wang , Jennifer Foster, and Yue Zhang

The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023 | [ arXiv ]
Exploring Vision-Language Models for Imbalanced Learning

Yidong Wang, Zhuohao Yu, Jindong Wang^# , Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, and Shikun Zhang

International Journal of Computer Vision (IJCV) 2023 | [ arXiv Code ]
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning

Kaijie Zhu, Xixu Hu, Jindong Wang , Xing Xie, and Ge Yang

International Conference on Computer Vision (ICCV) 2023 | [ arXiv PDF Code Zhihu ]
Boosting cross-domain speech recognition with self-supervision

Han Zhu, Gaofeng Cheng, Jindong Wang , Wenxin Hou, Pengyuan Zhang, and Yonghong Yan

IEEE Transactions on Audio, Speech and Language Processing (TASLP) 2023 | [ arXiv ]
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning

Xin Qin, Jindong Wang^# , Shuo Ma, Wang Lu, Yongchun Zhu, Xing Xie, and Yiqiang Chen

The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023 | [ arXiv Code Video ]
Domain-Specific Risk Minimization for Out-of-Distribution Generalization

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

The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023 | [ arXiv Code Video Zhihu ]
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, and Yue Zhang

The 61st Annual Meeting of the Association for Computational Linguistics (ACL) Findings 2023 | [ arXiv Code ]
Out-of-distribution Representation Learning for Time Series Classification

Wang Lu, Jindong Wang^# , Xinwei Sun, Yiqiang Chen, and Xing Xie

International Conference on Learning Representations (ICLR) 2023 | [ arXiv Code Website Zhihu ]
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

International Conference on Learning Representations (ICLR) 2023 | [ arXiv Code Website Zhihu ]

(600+ citations; Top 30 most cited ICLR papers in the past 5 years)
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

International Conference on Learning Representations (ICLR) 2023 | [ arXiv Code Website Zhihu ]

(300+ citations)
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning

Wang Lu, Xixu Hu, Jindong Wang^# , and Xing Xie

IEEE Data Engineering Bulletin 2023 | [ arXiv Code ]
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

ICLR workshop on Trustworthy and Reliable Large-Scale Machine Learning Models (ICLR 2023 workshop) 2023 | [ arXiv Code Zhihu ]

(Highlighted paper)
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

Yiqiang Chen, Wang Lu, Xin Qin, Jindong Wang , and Xing Xie

IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2023 | [ arXiv Code ]

(Innovation Award at IJCAI’23 FL workshop)

2022

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

Advances in Neural Information Processing Systems (NeurIPS) 2022 | [ arXiv Blog Code Zhihu ]

Margin Calibration for Long-Tailed Visual Recognition

Yidong Wang, Bowen Zhang, Wenxin Hou, Zhen Wu, Jindong Wang^# , and Takahiro Shinozaki

Asian Conference on Machine Learning (ACML) 2022 | [ arXiv Code Zhihu ]

@inproceedings{wang2022margin,
  title = {Margin Calibration for Long-Tailed Visual Recognition},
  author = {Wang, Yidong and Zhang, Bowen and Hou, Wenxin and Wu, Zhen and Wang, Jindong and Shinozaki, Takahiro},
  booktitle = {Asian Conference on Machine Learning (ACML)},
  year = {2022},
  arxiv = {https://arxiv.org/abs/2112.07225},
  bibtex_show = {true},
  corr = {true},
  zhihu = {https://zhuanlan.zhihu.com/p/579153319},
  code = {https://github.com/microsoft/robustlearn}
}

Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction

Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang , Takahiro Shinozaki, Manabu Okumura, and Yue Zhang

International Conference on Computational Linguistics (COLING) 2022 | [ arXiv PDF Code ]

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

IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022 | [ arXiv PDF Code Slides Website ]

(Top 9 most cited TKDE papers in the past 5 years)

@article{wang2022generalizing,
  title = {Generalizing to Unseen Domains: A Survey on Domain Generalization},
  author = {Wang, Jindong and Lan, Cuiling and Liu, Chang and Ouyang, Yidong and Qin, Tao and Lu, Wang and Chen, Yiqiang and Zeng, Wenjun and Yu, Philip S.},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2022},
  bibtex_show = {true},
  abbr = {TKDE},
  arxiv = {https://arxiv.org/abs/2103.03097},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG},
  slides = {DGtutorial_ijcai22.pdf},
  pdf = {DG_survey_TKDE22.pdf},
  website = {https://dgresearch.github.io/},
  special = {Top 9 most cited TKDE papers in the past 5 years}
}

Domain-invariant Feature Exploration for Domain Generalization

Wang Lu, Jindong Wang^# , Haoliang Li, Yiqiang Chen, and Xing Xie

Transactions on Machine Learning Research (TMLR) 2022 | [ arXiv PDF Code Website ]

@article{lu2022domain,
  title = {Domain-invariant Feature Exploration for Domain Generalization},
  author = {Lu, Wang and Wang, Jindong and Li, Haoliang and Chen, Yiqiang and Xie, Xing},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year = {2022},
  bibtex_show = {true},
  corr = {true},
  abbr = {TMLR},
  website = {https://openreview.net/forum?id=0xENE7HiYm},
  arxiv = {https://arxiv.org/abs/2207.12020},
  pdf = {tmlr22-difex.pdf},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG}
}

Domain generalization for activity recognition via adaptive feature fusion

Xin Qin, Jindong Wang^# , Yiqiang Chen, Wang Lu, and Xinlong Jiang

ACM Transactions on Intelligent Systems and Technology (TIST) 2022 | [ arXiv PDF ]

@article{qin2022domain,
  title = {Domain generalization for activity recognition via adaptive feature fusion},
  author = {Qin, Xin and Wang, Jindong and Chen, Yiqiang and Lu, Wang and Jiang, Xinlong},
  journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
  year = {2022},
  bibtex_show = {true},
  abbr = {TIST},
  corr = {true},
  arxiv = {https://arxiv.org/abs/2207.11221},
  pdf = {tist22-affar.pdf}
}

Decoupled Federated Learning for ASR with Non-IID Data

Han Zhu, Jindong Wang , Gaofeng Cheng, Pengyuan Zhang, and Yonghong Yan

Interspeech 2022 | [ arXiv PDF ]
Wav2vec-s: Semi-supervised pre-training for speech recognition

Han Zhu, Li Wang, Ying Hou, Jindong Wang , Gaofeng Cheng, Pengyuan Zhang, and Yonghong Yan

Interspeech 2022 | [ arXiv PDF ]

Personalized Federated Learning with Adaptive Batchnorm for Healthcare

Wang Lu, Jindong Wang^# , Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, and Tao Qin

IEEE Transactions on Big Data (TBD) 2022 | [ arXiv HTML PDF Code ]

@article{lu2022personalized,
  title = {Personalized Federated Learning with Adaptive Batchnorm for Healthcare},
  author = {Lu, Wang and Wang, Jindong and Chen, Yiqiang and Qin, Xin and Xu, Renjun and Dimitriadis, Dimitrios and Qin, Tao},
  journal = {IEEE Transactions on Big Data (TBD)},
  year = {2022},
  bibtex_show = {true},
  corr = {true},
  abbr = {TBD},
  arxiv = {https://arxiv.org/abs/2112.00734},
  pdf = {tbd22-fedap.pdf},
  html = {https://ieeexplore.ieee.org/document/9780172},
  code = {https://github.com/microsoft/PersonalizedFL}
}

Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition

Wang Lu, Jindong Wang^# , Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin

Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e., UbiComp) 2022 | [ arXiv PDF Code ]

@article{lu2022semantic,
  title = {Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition},
  author = {Lu, Wang and Wang, Jindong and Chen, Yiqiang and Pan, Sinno and Hu, Chunyu and Qin, Xin},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e., UbiComp)},
  year = {2022},
  abbr = {IMWUT},
  bibtex_show = {true},
  corr = {true},
  arxiv = {http://arxiv.org/abs/2206.06629},
  pdf = {imwut22-sdmix.pdf},
  code = {https://github.com/microsoft/robustlearn}
}

Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection

Yuxin Zhang, Jindong Wang^# , Yiqiang Chen, Han Yu, and Tao Qin

IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022 | [ arXiv PDF Code ]

@article{zhang2022adaptive,
  title = {Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection},
  author = {Zhang, Yuxin and Wang, Jindong and Chen, Yiqiang and Yu, Han and Qin, Tao},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2022},
  abbr = {TKDE},
  bibtex_show = {true},
  corr = {true},
  arxiv = {https://arxiv.org/abs/2201.00464},
  pdf = {tkde22_amsl.pdf},
  code = {https://github.com/zhangyuxin621/AMSL}
}

Exploiting Adapters for Cross-lingual Low-resource Speech Recognition

Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang^# , Tao Qin, Renjun Xu, and Takahiro Shinozaki

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 2022 | [ arXiv Code ]

@article{hou2022exploiting,
  title = {Exploiting Adapters for Cross-lingual Low-resource Speech Recognition},
  author = {Hou, Wenxin and Zhu, Han and Wang, Yidong and Wang, Jindong and Qin, Tao and Xu, Renjun and Shinozaki, Takahiro},
  journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
  year = {2022},
  abbr = {TASLP},
  bibtex_show = {true},
  arxiv = {https://arxiv.org/abs/2105.11905},
  corr = {true},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/ASR/Adapter}
}

Hierarchical knowledge amalgamation with dual discriminative feature alignment

Renjun Xu, Shuoying Liang, Lanyu Wen, Zhitong Guo, Xinyue Huang, Mingli Song, Jindong Wang , Xiaoxiao Xu, and Huajun Chen

Information Sciences 2022 | [ Website ]

Local and global alignments for generalizable sensor-based human activity recognition

Wang Lu, Jindong Wang^# , and Yiqiang Chen

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022 | [ HTML ]

@inproceedings{lu2022local,
  title = {Local and global alignments for generalizable sensor-based human activity recognition},
  author = {Lu, Wang and Wang, Jindong and Chen, Yiqiang},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year = {2022},
  bibtex_show = {true},
  corr = {true},
  abbr = {ICASSP},
  html = {https://ieeexplore.ieee.org/abstract/document/9747363}
}

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

44th International Conference on Software Engineering (ICSE) 2022 | [ PDF Code Video Zhihu ]

@inproceedings{zhang2022remos,
  title = {ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing},
  author = {Zhang, Ziqi and Li, Yuanchun and Wang, Jindong and Liu, Bingyan and Li, Ding and Chen, Xiangqun and Guo, Yao and Liu, Yunxin},
  booktitle = {44th International Conference on Software Engineering (ICSE)},
  year = {2022},
  bibtex_show = {true},
  abbr = {ICSE},
  pdf = {icse22-remos.pdf},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/ReMoS},
  zhihu = {https://zhuanlan.zhihu.com/p/446453487},
  video = {https://www.bilibili.com/video/BV1mi4y1C7bP}
}

2021

Unsupervised deep anomaly detection for multi-sensor time-series signals

Yuxin Zhang, Yiqiang Chen, Jindong Wang^# , and Zhiwen Pan

IEEE Transactions on Knowledge and Data Engineering (TKDE) 2021 | [ arXiv PDF Zhihu ]

(400+ citations; Top 80 most cited TKDE papers in the past 5 years)

@article{zhang2021unsupervised,
  title = {Unsupervised deep anomaly detection for multi-sensor time-series signals},
  author = {Zhang, Yuxin and Chen, Yiqiang and Wang, Jindong and Pan, Zhiwen},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2021},
  publisher = {IEEE},
  abbr = {TKDE},
  arxiv = {https://arxiv.org/abs/2107.12626},
  bibtex_show = {true},
  corr = {true},
  zhihu = {https://zhuanlan.zhihu.com/p/399411106},
  pdf = {tkde21.pdf},
  special = {400+ citations; Top 80 most cited TKDE papers in the past 5 years}
}

Cross-domain activity recognition via substructural optimal transport

Wang Lu, Yiqiang Chen, Jindong Wang , and Xin Qin

Neurocomputing 2021 | [ arXiv HTML PDF Code Zhihu ]

@article{lu2021cross,
  title = {Cross-domain activity recognition via substructural optimal transport},
  author = {Lu, Wang and Chen, Yiqiang and Wang, Jindong and Qin, Xin},
  journal = {Neurocomputing},
  volume = {454},
  pages = {65--75},
  year = {2021},
  publisher = {Elsevier},
  abbr = {NeuCom},
  bibtex_show = {true},
  arxiv = {https://arxiv.org/abs/2102.03353},
  html = {https://www.sciencedirect.com/science/article/abs/pii/S0925231221007025},
  zhihu = {https://zhuanlan.zhihu.com/p/356904023},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/traditional/sot},
  pdf = {neurocomputing21-sot.pdf}
}

Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling

Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang^# , Manabu Okumura, and Takahiro Shinozaki

Advances in Neural Information Processing Systems (NeurIPS) 2021 | [ arXiv PDF Code Slides Video Zhihu ]

(1400+ citations; Top 17 most cited NeurIPS papers in the past 5 years)

@article{zhang2021flexmatch,
  title = {Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling},
  author = {Zhang, Bowen and Wang, Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume = {34},
  year = {2021},
  bibtex_show = {true},
  corr = {true},
  abbr = {NeurIPS},
  arxiv = {https://arxiv.org/abs/2110.08263},
  pdf = {http://jd92.wang/assets/files/flexmatch_nips21.pdf},
  code = {https://github.com/TorchSSL/TorchSSL},
  zhihu = {https://zhuanlan.zhihu.com/p/422930830},
  video = {https://www.youtube.com/watch?v=aYuUwyZl_WY},
  slides = {https://www.jianguoyun.com/p/DXeFVg8QjKnsBRibj54E},
  selected = {true},
  special = {1400+ citations; Top 17 most cited NeurIPS papers in the past 5 years}
}

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

Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 | [ arXiv PDF Code ]

@inproceedings{liu2021learning,
  title = {Learning causal semantic representation for out-of-distribution prediction},
  author = {Liu, Chang and Sun, Xinwei and Wang, Jindong and Tang, Haoyue and Li, Tao and Qin, Tao and Chen, Wei and Liu, Tie-Yan},
  booktitle = {Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  bibtex_show = {true},
  abbr = {NeurIPS},
  arxiv = {https://arxiv.org/abs/2011.01681},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/CSG},
  pdf = {nips21-csusalcsg.pdf}
}

Adarnn: Adaptive learning and forecasting of time series

Yuntao Du, Jindong Wang^# , Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang

The 30th ACM International Conference on Information & Knowledge Management (CIKM) 2021 | [ arXiv PDF Code ]

(400+ citations; Paperdigest most influencial CIKM paper)

@inproceedings{du2021adarnn,
  title = {Adarnn: Adaptive learning and forecasting of time series},
  author = {Du, Yuntao and Wang, Jindong and Feng, Wenjie and Pan, Sinno and Qin, Tao and Xu, Renjun and Wang, Chongjun},
  booktitle = {The 30th ACM International Conference on Information \& Knowledge Management (CIKM)},
  pages = {402--411},
  year = {2021},
  bibtex_show = {true},
  abbr = {CIKM},
  corr = {true},
  selected = {true},
  arxiv = {https://arxiv.org/abs/2108.04443},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn},
  pdf = {cikm21-adarnn.pdf},
  special = {400+ citations; Paperdigest most influencial CIKM paper}
}

Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching

Wenxin Hou, Jindong Wang^# , Xu Tan, Tao Qin, and Takahiro Shinozaki

Interspeech 2021 | [ arXiv PDF Code ]

@inproceedings{hou2021cross,
  title = {Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching},
  author = {Hou, Wenxin and Wang, Jindong and Tan, Xu and Qin, Tao and Shinozaki, Takahiro},
  booktitle = {Interspeech},
  year = {2021},
  bibtex_show = {true},
  abbr = {IS},
  corr = {true},
  pdf = {a25_interspeech21.pdf},
  arxiv = {https://arxiv.org/abs/2104.07491},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/ASR/CMatch}
}

Generalizing to Unseen Domains: A Survey on Domain Generalization

Jindong Wang , Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin

International Joint Conference on Artificial Intelligence (IJCAI) 2021 | [ arXiv PDF Code Slides ]

@inproceedings{wang2021generalizing,
  title = {Generalizing to Unseen Domains: A Survey on Domain Generalization},
  author = {Wang, Jindong and Lan, Cuiling and Liu, Chang and Ouyang, Yidong and Zeng, Wenjun and Qin, Tao},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2021},
  bibtex_show = {true},
  abbr = {IJCAI},
  arxiv = {https://arxiv.org/abs/2103.03097},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG},
  slides = {DGSurvey-ppt.pdf},
  pdf = {DG_survey_TKDE22.pdf}
}

MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition

Linghui Meng, Jin Xu, Xu Tan, Jindong Wang , Tao Qin, and Bo Xu

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 | [ arXiv ]

@inproceedings{meng2021mixspeech,
  title = {MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition},
  author = {Meng, Linghui and Xu, Jin and Tan, Xu and Wang, Jindong and Qin, Tao and Xu, Bo},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {7008--7012},
  year = {2021},
  organization = {IEEE},
  abbr = {ICASSP},
  bibtex_show = {true},
  arxiv = {https://arxiv.org/pdf/2102.12664}
}

2020

Deep subdomain adaptation network for image classification

Yongchun Zhu, Fuzhen Zhuang, Jindong Wang , Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, and Qing He

IEEE transactions on neural networks and learning systems 2020 | [ HTML PDF Code ]

(1300+ citations; Top 12 most cited TNNLS papers in the past 5 years)

@article{zhu2020deep,
  title = {Deep subdomain adaptation network for image classification},
  author = {Zhu, Yongchun and Zhuang, Fuzhen and Wang, Jindong and Ke, Guolin and Chen, Jingwu and Bian, Jiang and Xiong, Hui and He, Qing},
  journal = {IEEE transactions on neural networks and learning systems},
  volume = {32},
  number = {4},
  pages = {1713--1722},
  year = {2020},
  publisher = {IEEE},
  bibtex_show = {true},
  pdf = {a24tnnls20.pdf},
  html = {https://ieeexplore.ieee.org/document/9085896},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/DeepDA},
  abbr = {TNNLS},
  special = {1300+ citations; Top 12 most cited TNNLS papers in the past 5 years}
}

Fedhealth: A federated transfer learning framework for wearable healthcare

Yiqiang Chen, Xin Qin, Jindong Wang , Chaohui Yu, and Wen Gao

IEEE Intelligent Systems 2020 | [ HTML PDF ]

(1300+ citations; most cited paper among all FL for healthcare papers)

@article{chen2020fedhealth,
  title = {Fedhealth: A federated transfer learning framework for wearable healthcare},
  author = {Chen, Yiqiang and Qin, Xin and Wang, Jindong and Yu, Chaohui and Gao, Wen},
  journal = {IEEE Intelligent Systems},
  volume = {35},
  number = {4},
  pages = {83--93},
  year = {2020},
  publisher = {IEEE},
  pdf = {a21_intesys20.pdf},
  html = {https://ieeexplore.ieee.org/document/9076082},
  bibtex_show = {true},
  special = {1300+ citations; most cited paper among all FL for healthcare papers}
}

Transfer learning with dynamic distribution adaptation

Jindong Wang , Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, and Qiang Yang

ACM Transactions on Intelligent Systems and Technology (TIST) 2020 | [ HTML PDF Code ]

(Top 8 most cited TIST paper in the past 5 years)

@article{wang2020transfer,
  title = {Transfer learning with dynamic distribution adaptation},
  author = {Wang, Jindong and Chen, Yiqiang and Feng, Wenjie and Yu, Han and Huang, Meiyu and Yang, Qiang},
  journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume = {11},
  number = {1},
  pages = {1--25},
  year = {2020},
  publisher = {ACM New York, NY, USA},
  pdf = {a17_tist19.pdf},
  html = {https://dl.acm.org/doi/abs/10.1145/3360309},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/DeepMEDA},
  bibtex_show = {true},
  abbr = {TIST},
  special = {Top 8 most cited TIST paper in the past 5 years}
}

Joint Partial Optimal Transport for Open Set Domain Adaptation.

Renjun Xu, Pelen Liu, Yin Zhang, Fang Cai, Jindong Wang , Shuoying Liang, Heting Ying, and Jianwei Yin

International Joint Conference on Artificial Intelligence (IJCAI) 2020 | [ HTML PDF ]

@inproceedings{xu2020joint,
  title = {Joint Partial Optimal Transport for Open Set Domain Adaptation.},
  author = {Xu, Renjun and Liu, Pelen and Zhang, Yin and Cai, Fang and Wang, Jindong and Liang, Shuoying and Ying, Heting and Yin, Jianwei},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {2540--2546},
  year = {2020},
  bibtex_show = {true},
  abbr = {IJCAI},
  html = {https://www.ijcai.org/Proceedings/2020/352},
  pdf = {a23_ijcai20.pdf}
}

Reliable weighted optimal transport for unsupervised domain adaptation

Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang^#

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020 | [ HTML PDF ]

@inproceedings{xu2020reliable,
  title = {Reliable weighted optimal transport for unsupervised domain adaptation},
  author = {Xu, Renjun and Liu, Pelen and Wang, Liyan and Chen, Chao and Wang, Jindong},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages = {4394--4403},
  year = {2020},
  abbr = {CVPR},
  bibtex_show = {true},
  html = {http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Reliable_Weighted_Optimal_Transport_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.html},
  pdf = {a22_cvpr20.pdf}
}

2019

Cross-dataset activity recognition via adaptive spatial-temporal transfer learning

Xin Qin, Yiqiang Chen, Jindong Wang , and Chaohui Yu

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 2019 | [ HTML PDF ]

(Top 47 most cited IMWUT papers in the past 5 years)

@article{qin2019cross,
  title = {Cross-dataset activity recognition via adaptive spatial-temporal transfer learning},
  author = {Qin, Xin and Chen, Yiqiang and Wang, Jindong and Yu, Chaohui},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
  volume = {3},
  number = {4},
  pages = {1--25},
  year = {2019},
  publisher = {ACM New York, NY, USA},
  pdf = {a20_ubicomp20.pdf},
  html = {https://dl.acm.org/doi/abs/10.1145/3369818},
  bibtex_show = {true},
  abbr = {IMWUT},
  special = {Top 47 most cited IMWUT papers in the past 5 years}
}

Transfer channel pruning for compressing deep domain adaptation models

Chaohui Yu, Jindong Wang , Yiqiang Chen, and Xin Qin

International Journal of Machine Learning and Cybernetics (JMLC) 2019 | [ HTML PDF Code ]

@article{yu2019transfer,
  title = {Transfer channel pruning for compressing deep domain adaptation models},
  author = {Yu, Chaohui and Wang, Jindong and Chen, Yiqiang and Qin, Xin},
  journal = {International Journal of Machine Learning and Cybernetics (JMLC)},
  volume = {10},
  number = {11},
  pages = {3129--3144},
  year = {2019},
  publisher = {Springer},
  html = {http://link.springer.com/article/10.1007/s13042-019-01004-6},
  pdf = {a18_ijmlc19.pdf},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/TCP},
  bibtex_show = {true},
  abbr = {JMLC}
}

Multi-representation adaptation network for cross-domain image classification

Yongchun Zhu, Fuzhen Zhuang, Jindong Wang , Jingwu Chen, Zhiping Shi, Wenjuan Wu, and Qing He

Neural Networks 2019 | [ HTML PDF Code ]

@article{zhu2019multi,
  title = {Multi-representation adaptation network for cross-domain image classification},
  author = {Zhu, Yongchun and Zhuang, Fuzhen and Wang, Jindong and Chen, Jingwu and Shi, Zhiping and Wu, Wenjuan and He, Qing},
  journal = {Neural Networks},
  volume = {119},
  pages = {214--221},
  year = {2019},
  publisher = {Elsevier},
  html = {https://doi.org/10.1016/j.neunet.2019.07.010},
  pdf = {a19_neunet19.pdf},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/MRAN},
  bibtex_show = {true},
  abbr = {NeuNet}
}

Cross-position activity recognition with stratified transfer learning

Yiqiang Chen, Jindong Wang , Meiyu Huang, and Han Yu

Pervasive and Mobile Computing 2019 | [ HTML PDF ]

@article{chen2019cross,
  title = {Cross-position activity recognition with stratified transfer learning},
  author = {Chen, Yiqiang and Wang, Jindong and Huang, Meiyu and Yu, Han},
  journal = {Pervasive and Mobile Computing},
  volume = {57},
  pages = {1--13},
  year = {2019},
  publisher = {Elsevier},
  html = {https://doi.org/10.1016/j.pmcj.2019.04.004},
  pdf = {a14_pmc19.pdf},
  bibtex_show = {true},
  abbr = {PMC}
}

Deep learning for sensor-based activity recognition: A survey

Jindong Wang , Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu

Pattern Recognition Letters 2019 | [ HTML PDF ]

@article{wang2019deep,
  title = {Deep learning for sensor-based activity recognition: A survey},
  author = {Wang, Jindong and Chen, Yiqiang and Hao, Shuji and Peng, Xiaohui and Hu, Lisha},
  journal = {Pattern Recognition Letters},
  volume = {119},
  pages = {3--11},
  year = {2019},
  publisher = {Elsevier},
  html = {https://www.sciencedirect.com/science/article/pii/S016786551830045X},
  pdf = {a10_prl18.pdf},
  bibtex_show = {true},
  abbr = {PRL}
}

Transfer learning with dynamic adversarial adaptation network

Chaohui Yu, Jindong Wang , Yiqiang Chen, and Meiyu Huang

2019 IEEE International Conference on Data Mining (ICDM) 2019 | [ PDF Code ]

(500+ citations; Top 4 most cited ICDM papers in the past 5 years)

@inproceedings{yu2019transfes,
  title = {Transfer learning with dynamic adversarial adaptation network},
  author = {Yu, Chaohui and Wang, Jindong and Chen, Yiqiang and Huang, Meiyu},
  booktitle = {2019 IEEE International Conference on Data Mining (ICDM)},
  pages = {778--786},
  year = {2019},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {ICDM},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/DAAN},
  pdf = {a16_icdm19.pdf},
  special = {500+ citations; Top 4 most cited ICDM papers in the past 5 years}
}

DrowsyDet: A Mobile Application for Real-time Driver Drowsiness Detection

Chaohui Yu, Xin Qin, Yiqiang Chen, Jindong Wang , and Chenchen Fan

Ubiquitous Intelligence Computing 2019 | [ ]

@inproceedings{yu2019drowsydet,
  title = {DrowsyDet: A Mobile Application for Real-time Driver Drowsiness Detection},
  author = {Yu, Chaohui and Qin, Xin and Chen, Yiqiang and Wang, Jindong and Fan, Chenchen},
  booktitle = {Ubiquitous Intelligence Computing},
  pages = {425--432},
  year = {2019},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {UIC}
}

Easy transfer learning by exploiting intra-domain structures

Jindong Wang , Yiqiang Chen, Han Yu, Meiyu Huang, and Qiang Yang

2019 IEEE International Conference on Multimedia and Expo (ICME) 2019 | [ PDF Code ]

(Top 3 most cited ICME papers in the past 5 years)

@inproceedings{wang2019easy,
  title = {Easy transfer learning by exploiting intra-domain structures},
  author = {Wang, Jindong and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yang, Qiang},
  booktitle = {2019 IEEE International Conference on Multimedia and Expo (ICME)},
  pages = {1210--1215},
  year = {2019},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {ICME},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/traditional/EasyTL},
  pdf = {a13_icme19.pdf},
  special = {Top 3 most cited ICME papers in the past 5 years}
}

Accelerating deep unsupervised domain adaptation with transfer channel pruning

Chaohui Yu, Jindong Wang , Yiqiang Chen, and Zijing Wu

2019 International Joint Conference on Neural Networks (IJCNN) 2019 | [ arXiv PDF Code ]

@inproceedings{yu2019accelerating,
  title = {Accelerating deep unsupervised domain adaptation with transfer channel pruning},
  author = {Yu, Chaohui and Wang, Jindong and Chen, Yiqiang and Wu, Zijing},
  booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},
  pages = {1--8},
  year = {2019},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {IJCNN},
  arxiv = {https://arxiv.org/pdf/1904.02654.pdf},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/TCP},
  pdf = {ijcnn19-tcp.pdf}
}

2018

OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition

Lisha Hu, Yiqiang Chen, Jindong Wang , Chunyu Hu, and Xinlong Jiang

International Journal of Machine Learning and Cybernetics (JMLC) 2018 | [ HTML PDF ]

@article{hu2018okrelm,
  title = {OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition},
  author = {Hu, Lisha and Chen, Yiqiang and Wang, Jindong and Hu, Chunyu and Jiang, Xinlong},
  journal = {International Journal of Machine Learning and Cybernetics (JMLC)},
  volume = {9},
  number = {9},
  pages = {1577--1590},
  year = {2018},
  publisher = {Springer},
  html = {https://link.springer.com/article/10.1007/s13042-017-0666-8?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst#enumeration},
  pdf = {a06_ijmlc.pdf},
  bibtex_show = {true},
  abbr = {JMLC}
}

Visual domain adaptation with manifold embedded distribution alignment

Jindong Wang , Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S Yu

The 26th ACM international conference on Multimedia 2018 | [ PDF Supp Code Poster ]

(900+ citations; 2nd most cited paper in MM’18)

@inproceedings{wang2018visual,
  title = {Visual domain adaptation with manifold embedded distribution alignment},
  author = {Wang, Jindong and Feng, Wenjie and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yu, Philip S},
  booktitle = {The 26th ACM international conference on Multimedia},
  pages = {402--410},
  year = {2018},
  bibtex_show = {true},
  abbr = {ACMMM},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA},
  pdf = {a11_mm18.pdf},
  supp = {https://www.jianguoyun.com/p/DRuWOFkQjKnsBRjkr2E},
  poster = {poster_mm18.pdf},
  selected = {true},
  special = {900+ citations; 2nd most cited paper in MM'18}
}

Deep transfer learning for cross-domain activity recognition

Jindong Wang , Vincent W Zheng, Yiqiang Chen, and Meiyu Huang

proceedings of the 3rd International Conference on Crowd Science and Engineering 2018 | [ HTML PDF ]

@inproceedings{wang2018deep,
  title = {Deep transfer learning for cross-domain activity recognition},
  author = {Wang, Jindong and Zheng, Vincent W and Chen, Yiqiang and Huang, Meiyu},
  booktitle = {proceedings of the 3rd International Conference on Crowd Science and Engineering},
  pages = {1--8},
  year = {2018},
  bibtex_show = {true},
  abbr = {ICCSE},
  pdf = {a12_iccse18.pdf},
  html = {https://dl.acm.org/citation.cfm?id=3265705}
}

Stratified transfer learning for cross-domain activity recognition

Jindong Wang , Yiqiang Chen, Lisha Hu, Xiaohui Peng, and S Yu Philip

IEEE international conference on pervasive computing and communications (PerCom) 2018 | [ PDF Code ]

@inproceedings{wang2018stratified,
  title = {Stratified transfer learning for cross-domain activity recognition},
  author = {Wang, Jindong and Chen, Yiqiang and Hu, Lisha and Peng, Xiaohui and Philip, S Yu},
  booktitle = {IEEE international conference on pervasive computing and communications (PerCom)},
  pages = {1--10},
  year = {2018},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {PerCom},
  code = {https://github.com/jindongwang/activityrecognition/tree/master/code/percom18_stl},
  pdf = {a09_percom18.pdf}
}

2017

Balanced distribution adaptation for transfer learning

Jindong Wang , Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen

IEEE international conference on data mining (ICDM) 2017 | [ HTML PDF Code ]

(700+ citations; Most cited paper in ICDM’17)

@inproceedings{wang2017balanced,
  title = {Balanced distribution adaptation for transfer learning},
  author = {Wang, Jindong and Chen, Yiqiang and Hao, Shuji and Feng, Wenjie and Shen, Zhiqi},
  booktitle = {IEEE international conference on data mining (ICDM)},
  pages = {1129--1134},
  year = {2017},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {ICDM},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/BDA},
  pdf = {a08_icdm17.pdf},
  html = {http://ieeexplore.ieee.org/document/8215613/?part=1},
  selected = {true},
  special = {700+ citations; Most cited paper in ICDM'17}
}

2016

Ocean: A new opportunistic computing model for wearable activity recognition

Yiqiang Chen, Yang Gu, Xinlong Jiang, and Jindong Wang^#

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct 2016 | [ HTML PDF ]

@inproceedings{chen2016ocean,
  title = {Ocean: A new opportunistic computing model for wearable activity recognition},
  author = {Chen, Yiqiang and Gu, Yang and Jiang, Xinlong and Wang, Jindong},
  booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct},
  pages = {33--36},
  year = {2016},
  bibtex_show = {true},
  abbr = {UbiComp},
  pdf = {a04_ubicomp16.pdf},
  html = {http://dl.acm.org/citation.cfm?id=2971453}
}

Less annotation on personalized activity recognition using context data

Lisha Hu, Yiqiang Chen, Shuangquan Wang, Jindong Wang , Jianfei Shen, Xinlong Jiang, and Zhiqi Shen

2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing (UIC) 2016 | [ HTML PDF ]

@inproceedings{hu2016less,
  title = {Less annotation on personalized activity recognition using context data},
  author = {Hu, Lisha and Chen, Yiqiang and Wang, Shuangquan and Wang, Jindong and Shen, Jianfei and Jiang, Xinlong and Shen, Zhiqi},
  booktitle = {2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing (UIC)},
  pages = {327--332},
  year = {2016},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {UIC},
  pdf = {a02_uic16.pdf},
  html = {http://ieeexplore.ieee.org/abstract/document/7816862/}
}