Partial List of Publications (by both graduates and undergraduates)
Autonomous Driving and Robotics:
Students (alumni): Xiaosong Jia, Zhenjie Yang, Penghao Wu, Shengchao Hu, Xiaolei Chen, Zhe Ren et al.
Machine Learning for Graph and Combinatorial Optimization:
Students (alumni): Runzhong Wang, Chang Liu, Yang Li, Haoyu Geng, Han Lu, Yixuan He (Oxford), et al.
Machine Learning for Science, Engineering and Arts (EDA, Drug, PDE, Neuro, Arts):
Students (alumni): {EDA: Ruoyu Cheng, Yang Li, Xingbo Du, Ruizhe Zhong, Peiyu Wang, Jianyong Yuan et al.} & {Drug/Chem/Neuro: Nianzu Yang, Kaipeng Zeng, Huaijin Wu et al.} & {PDE: Mingquan Feng, Zelin Zhao et al.}
  • Y. Wang, Y. Xia, Junchi Yan, Y. Yuan, H. Shen, X. Pan
    ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions.    
    Nature Communications (NC), 14 (1), 7861 2023.
  • M. Ma, Z. Jiang, T. Ma, X. Gao, J. Li, M. Liu, Junchi Yan (correspondence), X. Jinag
     Robust PUF Label Authentication System Synergistically Constructed by Hierarchical Pattern of Self-assembled Phase-Separation Encrypted Wrinkle and Deep Learning Model.    
    Advanced Functional Materials (AFM), 2405239, 2024.
  • N. Yang, K. Zeng, Y. Wu, H. Lu, Z. Yuan, F. Nie (本科生), Y. Li, S. Jiang, Y. Wang, Junchi Yan (correspondence).
    MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation.    
    International Conference on Machine Learning (ICML) oral, 2024.
  • H. Wu, W. Liu, Y. Bian, J. Wu, N. Yang, Junchi Yan (correspondence)
    EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model.    
    International Conference on Learning Representations (ICLR), 2024.
  • R. Zhong, J. Ye, Z. Tang, S. Kai, M. Yuan, J. Hao, Junchi Yan (correspondence)
    PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling.    
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
  • X. Chen (本科生), L. Yang, R. Wang, Junchi Yan (correspondence)
    MixSATGen: Learning Graph Mixing for SAT Instance Generation.    
    International Conference on Learning Representations (ICLR), 2024.
  • W. Wang, A. Liu (本科生), J. Ye, X. Li, L. Chen, M. Yuan, J. Hao, Junchi Yan (correspondence)
    EasyMap: Improving Technology Mapping via Exploration-Enhanced Heuristics and Adaptive Sequencing.    
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2023.
  • J. Yuan, J. Ye, Z. Tang, S. Kai, M. Yuan, J. Hao, Junchi Yan (correspondence)
    EasySO: Exploration-enhanced Reinforcement Learning for Logic Synthesis Sequence Optimization and a Comprehensive RL Environment.    
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2023.
  • X. Du, R. Zhang, S. Kai, Z. Tang, S. Xu, J. Hao, M. Yuan, Junchi Yan (correspondence).
    JigsawPlanner: Jiasaw-like Floorplanner for Elimincting Whitespace and Overamong Complex Reclinear Modules.    
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2024.
  • Y. Li, X. Chen (本科生), W. Guo, X. Li, W. Luo, J. Huang, H. Zeng, M. Yuan, Junchi Yan (correspondence)
    HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2023.
  • R. Cheng (本科生), Junchi Yan (correspondence).
    On Joint Learning for Solving Placement and Routing in Chip Design.
    Neural Information Processing Systems (NeurIPS), 2021
  • R. Cheng, X. Lv, Y. Li, J. Ye, J. Hao, Junchi Yan (correspondence).
    The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design.
    Neural Information Processing Systems (NeurIPS), 2022
  • X. Du, C. Wang (本科生), R. Zhong (本科生), Junchi Yan (correspondence).
    HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection.
    Neural Information Processing Systems (NeurIPS), 2023
  • N. Yang, K. Zeng (本科生), Q. Wu, X. Jia, Junchi Yan (correspondence)
    Learning Substructure Invariance for Out-of-Distribution Molecular Representations.
    Neural Information Processing Systems (NeurIPS) (spotlight), 2022
  • N. Yang, K. Zeng (本科生), Q. Wu, Junchi Yan (correspondence)
    MoleRec: Enhancing Medication Combination Recommendation with Substructure-Aware Molecule Learning.
    The ACM Web Conference (WWW), 2023
  • H. Xu, Junchi Yan (correspondence), N. Persson, W. Lin, H. Zha.
    Fractal Dimension Invariant Filtering and Its CNN-based Implementation.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
  • Y. Zhang, H. Li, S. Zhang, R. Wang, B. He, H. Dou, Junchi Yan, Y. Zhang, F. Wu
    LLMCO4MS: LLMs-aided Neural Combinatorial Solver for Ancient Manuscript Restoration from Fragments with Case Studies on Dunhuang.    
    European Conference on Computer Vision (ECCV), 2024.
  • Y. Zhang, Z. Fang, X. Yang, S. Zhang, B. He, H. Dou, Junchi Yan, Y. Zhang, F. Wu.
    Reconnecting the Broken Civilization: Patchwork Integration of Fragments from Ancient Manuscripts.    
    ACM Multimedia (MM), 2023.
  • W. Liu, T. He, C. Gong, N. Zhang, H. Yang, Junchi Yan.
    Fine-Grained Music Plagiarism Detection: Revealing Plagiarists through Bipartite Graph Matching and a Comprehensive Large-Scale Dataset.    
    ACM Multimedia (MM), 2023.
Quantum Machine Learning (especially for Graphs):
Students (alumni): Ge Yan, Xinyu Ye, Yehui Tang, Hao Xiong, Wenjie Wu, Xudong Lu et al.
  • G. Yan, W. Wu, Y. Chen (本科生), K. Pan (本科生), X. Lu, Z. Zhou, Y. Wang, R. Wang, Junchi Yan (correspondence)
    Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects.    
    arXiv preprint arXiv:2407.00736 (arXiv), 2024.
  • H. Xiong, Y. Tang, Y. He (本科生), W. Tan (本科生), Junchi Yan (correspondence)
    Node2ket: Efficient High-Dimensional Network Embedding in Quantum Hilbert Space.    
    International Conference on Learning Representations (ICLR), 2024.
  • G. Yan, H. Chen (本科生), K. Pan (本科生), Junchi Yan (correspondence).
    Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms.    
    International Conference on Learning Representations (ICLR), 2024.
  • Y. Tang, H. Xiong, N. Yang, T. Xiao, Junchi Yan (correspondence).
    Towards LLM4QPE: Unsupervised Pretraining of Quantum Property Estimation and A Benchmark.    
    International Conference on Learning Representations (ICLR) spotlight, 2024.
  • W. Wu, Y. Wang (本科生), G. Yan, Y. Zhao, Junchi Yan (correspondence).
    On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Program Scheduling.    
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2024.
  • Y. Tang, N. Yang, M. Long (本科生), Junchi Yan (correspondence).
    SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum System Certification.    
    International Conference on Machine Learning (ICML), 2024.
  • H. Xiong, Y. Tang, X. Ye, Junchi Yan (correspondence)
    Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  • Y. Tang, Junchi Yan (correspondence), G. Hu, B. Zhang, J. Zhou, Recent progress and perspectives on quantum computing for finance.    
    Service Oriented Computing and Applications, 16, 227–229, 2022.
  • X. Ye, G. Yan, Junchi Yan (correspondence)
    VQNE: Variational Quantum Network Embedding with Application to Network Alignment.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2023.
  • G. Yan, Y. Tang, Junchi Yan (correspondence).
    Towards a Native Quantum Paradigm for Graph Representation Learning: a Sampling-based Recurrent Embedding Approach.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2022.
  • X. Ye, G. Yan, Junchi Yan (correspondence).
    Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver.    
    International Conference on Machine Learning (ICML), 2023.
  • Y. Tang, Junchi Yan (correspondence).
    GraphQNTK: the Quantum Neural Tangent Kernel for Graph Data.    
    Neural Information Processing Systems (NeurIPS), 2022.
  • G. Yan, H. Wu, Junchi Yan (correspondence).
    Quantum 3D Graph Learning with Applications to Molecule Embedding.    
    International Conference on Machine Learning (ICML), 2023.
  • W. Wu, G. Yan, X. Lu (本科生), K. Pan (本科生), Junchi Yan (correspondence).
    QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms.    
    International Conference on Machine Learning (ICML), 2023.
  • X. Lu (本科生), K. Pan (本科生), G. Yan, J. Shan (本科生), W. Wu, Junchi Yan (correspondence).
    QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark.    
    International Conference on Machine Learning (ICML), 2023.
Graph Learning (e.g. Backbones, especially for scalable and robust learning):
Students (alumni): Qitian Wu, Chenxiao Yang, Chao Chen, Haoyu Geng, Tianqi Zhang et al.
  • W. Zhao, Q. Wu, C. Yang, Junchi Yan (correspondence)
    GeoMix: Towards Geometry-Aware Data Augmentation,
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2024
  • C. Yang, Q. Wu, D. Wipf, R. Sun, Junchi Yan (correspondence)
    How Graph Neural Networks Learn: Lessons from Training Dynamics,
    International Conference on Machine Learning (ICML), 2024
  • Q. Wu, F. Nie (本科生), C. Yang, Junchi Yan (correspondence),
    Learning Divergence Fields for Shift-Robust Graph Representations.    
    International Conference on Machine Learning (ICML), 2024.
  • Q. Wu, W. Zhao, C. Yang, H. Zhang, F. Nie (本科生), H. Jiang, Y. Bian, Junchi Yan (correspondence)
    SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
    Neural Information Processing Systems (NeurIPS), 2023
  • T. Zhang, Q. Wu, Junchi Yan (correspondence)
    ScaleGCN: Efficient and Effective Graph Convolution via Channel-wise Scale Transformation .    
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024, 35 (4), 4478-4490.
  • T. Zhang, Q. Wu, Junchi Yan (correspondence).
    Learning High-Order Graph Convolutional Networks via Adaptive Layerwise Aggregation Combination.    
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023, 34(8), 5144-5155.
  • C. Chen, D. Li, Junchi Yan (correspondence), H. Huang, X. Yang.
    Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Processing and Analysis.    
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • T. Bao, Q. Wu, Z. Jiang, Y. Chen, J. Sun, Junchi Yan (correspondence).
    Node Out-of-Distribution Detection Goes Neighborhood Shaping.    
    International Conference on Machine Learning (ICML), 2024.
  • Q. Wu, F. Nie (本科生), C. Yang, T. Bao, Junchi Yan (correspondence)
    GraphSHINE: Training Shift-Robust Graph Neural Networks with Environment Inference.
    The ACM Web Conference (WWW), 2024
  • Q. Wu, H. Zhang (本科生), Junchi Yan (correspondence), D. Wipf
    Handling Distribution Shifts on Graphs: An Invariance Perspective   
    International Conference on Learning Representations (ICLR), 2022.
  • C. Chen, H. Geng, G. Zeng, Z. Han, H. Chai, X. Yang, Junchi Yan (correspondence),
    Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution.    
    International Conference on Learning Representations (ICLR), 2023.
  • H. Geng, C. Chen, Y. He, G. Zeng, Z. Han, H. Chai, Junchi Yan (correspondence)
    Pyramid Graph Neural Network: a Graph Sampling and Filtering Approach for Multi-scale Disentangled Representations.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2023.
  • W. Zhao, Q. Wu, C. Yang, Junchi Yan (correspondence)
    GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2023.
  • Q. Wu, Y. Chen, C. Yang, Junchi Yan (correspondence),
    Energy-based Out-of-Distribution Detection for Graph Neural Networks.    
    International Conference on Learning Representations (ICLR), 2023.
  • Q. Wu, C. Yang, W. Zhao, Y. He, D. Wipf, Junchi Yan (correspondence),
    DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion.    
    International Conference on Learning Representations (ICLR), 2023 (spotlight).
  • D. Lao (本科生), X. Yang (本科生), Q. Wu, Junchi Yan (correspondence).
    Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2022.
  • C. Yang, Q. Wu, J. Wang, Junchi Yan
    Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and Multi-Layer Perceptrons .    
    International Conference on Learning Representations (ICLR), 2023.
  • Q. Wu, W. Zhao (本科生), Z. Li (本科生), D. Wipf, Junchi Yan (correspondence).
    NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification.
    Neural Information Processing Systems (NeurIPS) (spotlight), 2022
  • Z. Li (本科生), Q. Wu, F. Nie (本科生), Junchi Yan (correspondence).
    GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs.
    Neural Information Processing Systems (NeurIPS), 2022
  • C. Yang, Q. Wu, Junchi Yan (correspondence).
    Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks.    
    Neural Information Processing Systems (NeurIPS), 2022.
Discrete Time Space (Time Series) Learning:
Students (alumni): Yunhao Zhang, Longyuan Li et al.
  • Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, Junchi Yan, L. Sun.
    Transformers in Time Series: A Survey.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2023.
  • Y. Zhang, M. Liu (本科生), S. Zhou (本科生), Junchi Yan (correspondence).
    UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis.    
    International Conference on Machine Learning (ICML), 2024.
  • Y. Zhang, Junchi Yan (correspondence)
    Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting.   
    International Conference on Learning Representations (ICLR), 2023 (oral).
  • N. Zhang, Junchi Yan (correspondence), Y. Zhou.
    Weakly-supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2018.
  • Junchi Yan, C. Tian, J. Huang, F. Albertao.
    Incremental Dictionary Learning for Fault Detection with Applications to Oil Pipeline Leakage Detection.    
    IET Electronics Letters (EL), 2011, 47 (21), 1198-1199.
  • L. Li, J. Yao, L. K. Wenliang, T. He, T. Xiao, Junchi Yan (correspondence), D. Wipf, Z. Zhang.
    GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction.
    Neural Information Processing Systems (NeurIPS), 2021
  • L. Li, Junchi Yan (correspondence), Y. Zhang (本科生), J. Zhang (本科生), J. Bao, Y. Jin, X. Yang.
    Learning Generative RNN-ODE for Collaborative Time-Series and Event Sequence Forecasting.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 35(7): 7118-7137, 2023
  • L. Li, Junchi Yan (correspondence), Q. Wen, Y. Jin, X. Yang
    Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 35(6): 6058-6072, 2023
  • L. Li, Junchi Yan (correspondence), H. Wang, Y. Jin.
    Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder.    
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021, 32 (3), 1177-1191.
  • C. Yang, Q. Wu, Q. Wen, Z. Zhou, L. Sun, Junchi Yan (correspondence).
    Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment.
    Neural Information Processing Systems (NeurIPS), 2022
  • L. Li, Junchi Yan (correspondence), X. Yang, Y. Jin.
    Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2019.
Continuous Time Space (Temporal Point Process) Learning:
Students (alumni): Yunhao Zhang, Mingquan Feng, Chao Chen, Shuai Xiao, Weichang Wu, Fangyu Ding et al.
  • Q. Wu, Z. Zhang, X. Gao, Junchi Yan, G. Chen.
    Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling.
    Neural Information Processing Systems (NeurIPS), 2019
  • C. Chen, D. Li, Junchi Yan (correspondence), X. Yang.
    Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2022, 34(11): 5446-5458.
  • S. Li, M. Feng (本科生), L. Wang, A. Essofi, Y. Cao, Junchi Yan , L. Song
    Explaining Point Processes by Learning Interpretable Temporal Logic Rules.    
    International Conference on Learning Representations (ICLR), 2022.
  • S. Xiao, Junchi Yan (correspondence), M. Farajtabar, L. Song, X. Yang, H. Zha.
    Learning Time Series Associated Event Sequences with Recurrent Point Process Networks.    
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019, 30(10): 3124-3136.
  • W. Wu, Junchi Yan (correspondence), X. Yang, H. Zha.
    Discovering Temporal Patterns for Event Sequence Clustering via Policy Mixture Model.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2022, 34(2): 573-586.
  • W. Wu, Junchi Yan (correspondence), X. Yang, H. Zha.
    Decoupled Learning for Factorial Marked Temporal Point Processes.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2018.
  • Junchi Yan, X. Liu, L. Shi, C. Li, H. Zha.
    Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2018.
  • S. Xiao, H. Xu, Junchi Yan (correspondence), M. Farajtabar, X. Yang, L. Song, H. Zha.
    Learning Conditional Generative Models for Temporal Point Processes.    
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
  • S. Xiao, M. Farajtabar, X. Ye, Junchi Yan, L. Song, H. Zha.
    Wasserstein Learning of Deep Generative Point Process Models.    
    Neural Information Processing Systems (NIPS), 2017.
  • Junchi Yan, S. Xiao, C. Li, B. Jin, X. Wang, B. Ke, X. Yang, H. Zha.
    Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2016.
  • C. Chen, H. Geng, N. Yang, Junchi Yan (correspondence), D. Xue, J. Yu, X. Yang.
    Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation.    
    International Conference on Machine Learning (ICML), 2021.
  • Junchi Yan, C. Zhang, H. Zha, M. Gong, C. Sun, J. Huang, S. Chu, X. Yang.
    On Machine Learning towards Predictive Sales Pipeline Analytics.    
    AAAI Conference on Artificial Intelligence (AAAI), 2015.
  • Junchi Yan, Y. Wang, K. Zhou, J. Huang, C. Tian, H. Zha, W. Dong.
    Towards Effective Prioritizing Water Pipe Replacement and Rehabilitation.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2013.
  • X. Liu, Junchi Yan (correspondence), S. Xiao, X. Wang, H. Zha, S. Chu.
    On Predictive Patent Valuation: Forecasting Patent Citations and Their Types.    
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
  • S. Xiao, Junchi Yan (correspondence), X. Yang, H. Zha, S. Chu.
    Modeling the Intensity Function of Point Process via Recurrent Neural Networks.    
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
  • S. Xiao, Junchi Yan (correspondence), C. Li, B. Jin, X. Wang, H. Zha, X. Yang, S. Chu.
    On Modeling and Predicting Individual Paper Citation Count over Time.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2016.
  • Y. Zhang, Junchi Yan (correspondence), X. Zhang, J. Zhou, X. Yang
    Learning Mixture of Neural Temporal Point Processes for Multi-dimensional Event Sequence Clustering.   
    International Joint Conferences on Artificial Intelligence (IJCAI), 2022.
  • Y. Zhang (本科生), Junchi Yan (correspondence).
    Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2021.
  • F. Ding, Junchi Yan (correspondence), H. Wang
    c-NTPP: Learning Cluster-aware Neural Temporal Point Process.    
    AAAI Conferenc on Artificial Intelligence (AAAI), 2023.
  • X. Wang, S. Chen, Y. He, M. Wang, Q. Gan, Junchi Yan
    CEP3: Community Event Prediction with Neural Point Process on Graph.  
    The First Learning on Graphs Conference(LoG), 2023.
  • L. Li, J. Zhang (本科生), Junchi Yan (correspondence), Y. Jin, Y. Zhang (本科生), Y. Duan, G. Tian.
    Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting.    
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
Automatic Neural Architecture Search and Optimization:
Students (alumni): Xiaoxing Wang, Zhexi Zhang et al.
  • X. Wang, Z. Lian, J. Lin, C. Xue, Junchi Yan (correspondence)
    DIY Your EasyNAS for Vision: Convolution Operation Merging, Map Channel Reducing, and Search Space to Supernet Conversion Tooling.    
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023: 45(11), 13974 - 13990
  • B. Zhang (本科生), X. Wang, X. Qin (本科生), Junchi Yan (correspondence).
    Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  • X. Wang, X. Chu, Y. Fan, Z. Zhang, B. Zhang, X. Wei, X. Yang, Junchi Yan (correspondence)
    ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation.    
    IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
  • X. Wang, W. Guo (本科生), J. Su, X. Yang, Junchi Yan (correspondence)
    ZARTS: On Zero-order Optimization for Neural Architecture Search.
    Neural Information Processing Systems (NeurIPS), 2022
  • C. Xue, X. Wang, Junchi Yan (correspondence), Y. Hu, X. Yang, K. Sun.
    Rethinking Bi-Level Optimization in Neural Architecture Search: a Gibbs Sampling Perspective.    
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • X. Chu, X. Wang, B. Zhang, S. Lu, X. Wei, Junchi Yan
    DARTS-: Robustly Stepping out of Performance Collapse Without Indicators.    
    International Conference on Learning Representations (ICLR), 2021.
  • X. Wang, J. Lin, J. Zhao, X. Yang, Junchi Yan (correspondence).
    EAutoDet: Efficient Architecture Search for Object Detection.  
    European Conference on Computer Vision (ECCV), 2022.
  • C. Xue, Junchi Yan, R. Yan, S. Chu, Y. Lin, Y. Hu.
    Transferable AutoML by Model Sharing over Grouped Datasets.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  • C. Xue, X. Wang, Junchi Yan, C. Li.
    A Max-Flow based Approach for Neural Architecture Search .    
    European Conference on Computer Vision (ECCV), 2022.
  • Z. Zhang, W. Zhu, Junchi Yan, P. Gao and G. Xie
    Automatic Student Network Search for Knowledge Distillation.    
    International Conference on Pattern Recognition (ICPR), 2020.
  • X. Wang, C. Xue, Junchi Yan (correspondence), X. Yang, Y. Hu, K. Sun.
    MergeNAS: Merge Operations into One for Differentiable Architecture Search.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2020.
Classic Algorithms for Graph and Combinatorial Optimization:
Students (alumni): Tianshu Yu (ASU), Zetian Jiang, Tianzhe Wang et al.
  • 严骏驰, 杨小康
    计算机视觉中图匹配研究进展: 从二图匹配迈向多图匹配.   
    控制理论与应用 (CCTA), 2018年第十二期 [pdf], 2018
  • C. Liu, C. Lou (本科生), R. Wang, A. Xi (本科生), L. Shen, Junchi Yan (correspondence).
    Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning.    
    International Conference on Machine Learning (ICML), 2022.
  • Z. Jiang (本科生), T. Wang (本科生), Junchi Yan (correspondence)
    Unifying Offline and Online Multi-graph Matching via Finding Shortest Paths on Supergraph.   
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021, 43(10): 3648-3663
  • Junchi Yan, M. Cho, H. Zha, X. Yang, S. Chu
    Multi-Graph Matching via Affinity Optimization with Graduated Consistency Regularization.   
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016, 38(6): 1228-1242
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  • Junchi Yan, C. Li, Y. Li, G. Cao
    Adaptive Discrete Hypergraph Matching.   
    IEEE Transactions on Cybernetics (TCYB), 2018, 48(2): 765-779
  • Junchi Yan, J. Wang, H. Zha, X. Yang, S. Chu.
    Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach.    
    IEEE Transactions on Image Processing (TIP), 2015, 24(3): 994-1009.
    code
  • Junchi Yan, Z. Ren, H. Zha, S. Chu.
    A Constrained Clustering based Approach for Matching a Collection of Feature Sets,.     
    IEEE International Conference on Pattern Recognition (ICPR), 2016.
  • Junchi Yan, X. Yin, W. Lin, C. Deng, H. Zha, X. Yang.
    A Short Survey of Recent Advances in Graph Matching.    
    ACM International Conference on Multimedia Retrieval (ICMR oral), 2016.
  • T. Yu, Junchi Yan, W. Liu, B. Li.
    Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering .    
    European Conference on Computer Vision (ECCV), 2018.
  • Junchi Yan, Y. Li, W. Liu, H. Zha, X. Yang, S. Chu.
    Graduated Consistency-regularized Optimization for Multi-graph Matching.    
    European Conference on Computer Vision (ECCV), 2014.
  • Junchi Yan, Y. Tian, H. Zha, X. Yang, Y. Zhang, S. Chu.
    Joint Optimization for Consistent Multiple Graph Matching.    
    IEEE International Conference on Computer Vision (ICCV), 2013.
  • Y. Tian, Junchi Yan (correspondence), H. Zhang, Y. Zhang, X. Yang, H. Zha.
    On the Convergence of Graph Matching: Graduated Assignment Revisited.    
    European Conference on Computer Vision (ECCV), 2012.
  • T. Yu, Junchi Yan (correspondence), J. Zhao, B. Li.
    Joint Cuts and Matching of Partitions in One Graph.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  • T. Yu, Junchi Yan, B. Li
    Determinant Regularization for Gradient-Efficient Graph Matching.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  • T. Wang (本科生), Z. Jiang (本科生), Junchi Yan (correspondence).
    Clustering-aware Multiple Graph Matching via Decayed Pairwise Matching Composition.    
    AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Z. Chen (本科生), Z. Xie (本科生), Junchi Yan (correspondence), Y. Zheng, X. Yang.
    Layered Neighborhood Expansion for Incremental Multiple Graph Matching.    
    European Conference on Computer Vision (ECCV), 2020.
  • Junchi Yan, H. Xu, H. Zha, X. Yang, S. Chu.
    A Matrix Decomposition Perspective to Multiple Graph Matching.    
    IEEE International Conference on Computer Vision (ICCV), 2015.
  • T. Yu, Junchi Yan, Y. Wang, W. Liu, B. Li.
    Generalizing Graph Matching beyond Quadratic Assignment Model.    
    Neural Information Processing Systems (NIPS), 2018.
  • Junchi Yan, C. Zhang, H. Zha, W. Liu, X. Yang, S. Chu.
    Discrete Hyper-graph Matching.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
  • Junchi Yan, J. Wang, H. Zha, X. Yang, S. Chu.
    Multi-view Point Registration via Alternating Optimization.    
    AAAI Conference on Artificial Intelligence (AAAI), 2015.
Visual (Rotating) Object Detection:
Students (alumni): Xue Yang et al.
  • 杨学, 严骏驰 (通讯作者)
    基于特征对齐和高斯表征的视觉有向目标检测.   
    中国科学: 信息科学 (SSI), 2023, 53(11):2250 [pdf], 2023
  • X. Yang, Junchi Yan (correspondence)
    On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited.   
    International Journal of Computer Vision (IJCV) 130(5), 1340–1365, 2022 [project page]
  • X. Yang, Junchi Yan (correspondence), W. Liao, X. Yang, J. Tang, T. He
    SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing. 
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2), 2384-2399, 2023 [project page]
  • X. Yang, G. Zhang, X. Yang, Y. Zhou, W. Wang, T. He, J. Tang, Junchi Yan (correspondence)
    Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization.   
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 45(4): 4335-4354, 2023
  • Y. Zeng, Y. Chen, X. Yang, Q. Li, Junchi Yan
    ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection.   
    IEEE Transactions on Geoscience and Remote Sensing (TGRS) 62: 1-15, 2024
  • J. Luo, X. Yang, Y. Yu, Q. Li,, Junchi Yan, Y. Li.
    PointOBB: Learning Oriented Object Detection via Single Point Supervision.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  • Y. Yu, X. Yang, Q. Li, F. Da, J. Dai, Y. Qiao, Junchi Yan (correspondence).
    Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  • Z. Xiao, G. Yang, X. Yang, T. Mu, Junchi Yan, S. Hu.
    Theoretically Achieving Continuous Representation of Oriented Bounding Boxes.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  • X. Yang, Y. Zhou, Junchi Yan (correspondence)
    AlphaRotate: A rotation detection benchmark using tensorflow.
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
  • Yi Yu, Xue Yang, Qingyun Li, Yue Zhou, Gefan Zhang, Feipeng Da, Junchi Yan (correspondence)
    H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection.
    Neural Information Processing Systems (NeurIPS), 2023
  • X. Yang, Y. Zhou, G. Zhang, J. Yang, W. Wang, Junchi Yan (correspondence), X. Zhang, Q. Tian
    The KFIoU Loss for Rotated Object Detection.    
    International Conference on Learning Representations (ICLR), 2023.
  • X. Yang, G. Zhang, W. Li, Y. Zhou, X. Wang, Junchi Yan (correspondence)
    H2RBox: Horizonal Box Annotation is All You Need for Oriented Object Detection.    
    International Conference on Learning Representations (ICLR), 2023.
  • X. Yang, X. Yang, J. Yang, Q. Ming, W. Wang, Q. Tian, Junchi Yan (correspondence).
    Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence.
    Neural Information Processing Systems (NeurIPS), 2021
  • X. Yang, Junchi Yan (correspondence), Q. Ming, W. Wang, X. Zhang, Q. Tian.
    Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss.    
    International Conference on Machine Learning (ICML), 2021.
  • X. Yang, L. Hou, Y. Zhou, W. Wang, Junchi Yan (correspondence).
    Dense Label Encoding for Boundary Discontinuity Free Rotation Detection.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  • X. Yang, Junchi Yan (correspondence).
    Arbitrary-Oriented Object Detection with Circular Smooth Label.    
    European Conference on Computer Vision (ECCV), 2020.
  • Y. Zhou, X. Yang, G. Zhang, Junchi Yan (correspondence) et al.
    MMRotate: A Rotated Object Detection Benchmark using Pytorch.    
    ACM Multimedia (MM), 2022 (OS Track).
  • Q. Wen, X. Yang, S. Peng, M. Song, Junchi Yan.
    RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection.    
    IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2022
  • W. Qian, X. Yang, S. Peng, Junchi Yan, Y. Guo.
    Learning Modulated Loss for Rotated Object Detection.    
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • X. Yang, Junchi Yan (correspondence), Z. Feng, T. He.
    R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object.    
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • X. Yang, J. Yang, Junchi Yan (correspondence), Y. Zhang, T. Zhang, Z. Guo, X. Sun, K. Fu
    SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects    
    International Conference on Computer Vision (ICCV), 2019.
  • X. Yu, P. Chen, D. Wu, N. Hassan, G. Li, Junchi Yan, H. Shi, Q. Ye, Z. Han
    Object Localization under Single Coarse Point Supervision.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Robust and Trustable AI:
Students (alumni): Ruoxi Chen, Haoxuan Wang, Yiting Chen, Ning Liao, Yucheng Luo, Huaqing Shao et al.
  • Q. Wu, C. Yang, Junchi Yan (correspondence).
    Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach.
    Neural Information Processing Systems (NeurIPS), 2021
  • H. Shao, L. Wang, Y. Wang, Q. Ren, Junchi Yan (correspondence)
    Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range.   
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2024.
  • Q. Ren, Y. Chen, Y. Mo (本科生), Q. Wu, Junchi Yan (correspondence).
    DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2022.
  • H. Shao, L. Wang, Junchi Yan (correspondence)
    Robustness Certification for Structured Prediction with General Inputs via Safe Region Modeling in the Semimetric Output Space.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2023.
  • M. Li, C. Deng, T. Li, Junchi Yan, X. Gao, H. Huang
    Towards Transferable Targeted Attack.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (spotlight), 2020.
  • R. Chen, Z. Li (本科生), J. Li, C. Wu, Junchi Yan .
    On Collective Robustness of Bagging Against Data Poisoning.    
    International Conference on Machine Learning (ICML), 2022.
  • H. Wang, Z. Yu, Y. Yue, A. Anandkumar, A. Liu, Junchi Yan
    Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2023.
  • Y. Chen, Q. Ren, Junchi Yan (correspondence).
    Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain.
    Neural Information Processing Systems (NeurIPS) (spotlight), 2022
  • J. Hu, H. Zhong, G. Wu, F. Yang, S. Gong, Junchi Yan
    Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling.    
    European Conference on Computer Vision (ECCV), 2022.
  • J. Hu, H. Tuo, C. Wang, L. Qiao, H. Zhong, Junchi Yan, Z. Jing, H. Leung.
    Discriminative Partial Domain Adversarial Network.    
    European Conference on Computer Vision (ECCV), 2020.
  • R. Chen, J. Li, Junchi Yan, P. Li, B. Sheng.
    Input-specific Robustness Certification for Randomized Smoothing.    
    AAAI Conference on Artificial Intelligence (AAAI), 2022.
  • N. Liao, Y. Liu, X. Li, C. Lei, G. Wang, X. Hua, Junchi Yan (correspondence).
    CoHOZ: Contrastive Multimodal Prompt Tuning for Hierarchical Open-set Zero-shot Recognition.    
    ACM Multimedia (MM), 2022.
  • Y. Luo, Y. Zhang, Junchi Yan (correspondence), W. Liu,
    Generalizing Face Forgery Detection with High-frequency Features.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  • Z. Xiong, L. Li, Junchi Yan, H. Wang, H. He, Y. Jin.
    Differential Privacy with Variant-Noise for Gaussian Processes Classification.    
    Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2019.
  • G. Wang, X. Wu, Z. Liu, Junchi Yan (correspondence).
    Prompt-based Zero-shot Video Moment Retrieval.    
    ACM Multimedia (MM), 2022.
  • Y. Luo, J. Zhu, K. He, W. Chu, Y. Tai, Junchi Yan (correspondence), C. Wang.
    StyleFace: Towards Identity-Disentangled Face Generation on Megapixels.    
    European Conference on Computer Vision (ECCV), 2022.
Recommender Systems and Collaborative Filtering:
Students (alumni): Chao Chen, Qitian Wu, et al.
  • W. Xu, Q. Wu, R. Wang, M. Ha, Q. Ma, L. Chen, B. Han, Junchi Yan (correspondence)
    Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions.
    The ACM Web Conference (WWW), 2024 oral
  • Q. Wu, H. Zhang, X. Gao, Junchi Yan , H. Zha.
    Towards Open-World Recommender Systems: A Model-based Collaborative Filtering Approach.    
    International Conference on Machine Learning (ICML), 2021.
  • C. Chen, D. Li, Q. Lv, Junchi Yan (mentor), L. Shang, S. Chu.
    GLOMA: Embedding Global Information in Local Matrix Approximation Models for Collaborative Filtering.    
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
  • D. Li, C. Chen, Q. Lv, Junchi Yan, L. Shang, S. Chu.
    Low-Rank Matrix Approximation with Stability.    
    International Conference on Machine Learning (ICML), 2016.
  • C. Chen, D. Li, Q. Lv, Junchi Yan (mentor), S. Chu, L. Shang.
    MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2016.
  • C. Chen, H. Zhang, D. Li, Junchi Yan, X. Yang
    Synergizing Local and Global Models for Matrix Approximation .    
    ACM International Conference on Conference on Knowledge Management (CIKM), 2019.
Machine Learning Basics and Miscellaneous:
  • Z. Zhou, Z. Chen, Y. Chen, B. Zhang, Junchi Yan (correspondence)
    Cross-Task Linearity Emerges in the Pretraining-Finetuning Paradigm.    
    International Conference on Machine Learning (ICML), 2024.
  • H. Zhao, X. Yang. Junchi Yan, C. Deng
    Dynamic Cognition-aware Spiking Graph Neural Network
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
  • D. Lao, Q. Liu (本科生). J. Bu (本科生). Junchi Yan (correspondence) W. Shen
    ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
  • J. Sun, K. Li, R. Chen, J. Li, C. Wu, Y. Ding, Junchi Yan
    InterpGNN: Understand and Improve Generalization Ability of Transdutive GNNs through the Lens of Interplay between Train and Test Nodes,
    International Conference on Learning Representations (ICLR), 2024
  • Y. Chen, Z. Zhou, Junchi Yan (correspondence)
    Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory,
    International Conference on Learning Representations (ICLR), 2024
  • Z. Zhou, Y. Yong, X. Yang, Junchi Yan (correspondence), W. Hu
    Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity.
    Neural Information Processing Systems (NeurIPS), 2023
  • Y. Hua, X. Wang, B. Jin, W. Li, Junchi Yan, X. He, H. Zha
    HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem.   
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2021
  • C. Li, X. Wang, W. Dong, Junchi Yan, Q. Liu, H. Zha
    Joint Active Learning with Feature Selection via CUR Matrix Decomposition.   
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019, 41 (6), 1382-1396
  • Y. Li, Junchi Yan, Y. Zhou, J. Yang.
    Optimum Subspace Learning and Error Correction for Tensors.    
    European Conference on Computer Vision (ECCV), 2010.
  • X. Xie, H. Lu, Junchi Yan, X. Yang, M. Tomizuka, W. Zhan
    Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  • W. Zhang, Junchi Yan, X. Wang, H. Zha.
    Deep Extreme Multi-Label Learning.    
    ACM International Conference on Multimedia Retrieval (ICMR oral), 2018.
  • S. Zhang (本科生), M. Liu (本科生), Junchi Yan (correspondence).
    The Diversified Ensemble Neural Network.    
    Neural Information Processing Systems (NeurIPS), 2020.
  • C. Li, Junchi Yan (correspondence), F. Wei, W. Dong, Q. Liu, H. Zha.
    Self-paced Multi-task Learning.  
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
  • C. Li, F. Wei, Junchi Yan (correspondence), X. Zhang, Q. Liu, H. Zha.
    A Self-Paced Regularization Framework for Multi-Label Learning.    
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018, 29 (6), 2660-2666.
  • X. Yang, C. Deng, F. Zheng, Junchi Yan, W. Liu.
    Deep Spectral Clustering using Dual Autoencoder Network.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  • A. Zhang, N. Li, J. Pu, J. Wang, Junchi Yan, H. Zha.
    τ-FPL: Tolerance-Constrained Learning in Linear Time.    
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
  • J. Zhu, L. Shi, Junchi Yan (correspondence), H. Zha.
    AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning.    
    European Conference on Computer Vision (ECCV), 2020.
Multi-agent System and Distributed Optimization:
Students (alumni): Wenhao Li (ECNU) et al.
  • X. Wang. Junchi Yan, B. Jin., W. Li.
    Distributed and Parallel ADMM for Structured Nonconvex Optimization Problem
    .    
    IEEE Transactions on Cybernetics (TCYB), 2021, 51(9): 4540-4552.
  • J. Sheng, X. Wang, B. Jin, Junchi Yan , W. Li, T. H. Chang, J. Wang, H. Zha
    Learning Structured Communication for Multi-agent Reinforcement Learning.   
    Autonomous Agents and Multi-Agent Systems (AAMAS) 2021 [arxiv]
  • W. Li, B. Jin, X. Wang, Junchi Yan, H. Zha
    F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning.   
    Journal of Machine Learning Research (JMLR) 24(178): 1−75, 2023 [arxiv]
  • X. Wang, W. Zhang, Junchi Yan, X. Yuan, H. Zha
    On the Flexibility of Block Coordinate Descent for Large-Scale Optimization.    
    Neurocomputing (NC), 2018.
Generative Models, Optimal Transport and Self-supervised Learning:
Students (alumni): Yang Li, Shaofeng Zhang, Liangliang Shi and Xiaojiang Yang et al.
  • L. Shi, J. Fan (本科生), Junchi Yan (correspondence)
    OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport.    
    International Conference on Machine Learning (ICML), 2024.
  • Z. Zhao, F. Fan, W. Liao, Junchi Yan.
    Grounding and Enhancing Grid-based Models for Neural Fields.    
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Best Paper Candidate
  • S. Zhang, J. Huang, Q. Zhou, Z. Wang, F. Wang, J. Luo, Junchi Yan (correspondence).
    Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach.
    International Conference on Learning Representations (ICLR), 2024
  • L. Shi, H. Zhen (本科生), G. Zhang (本科生), Junchi Yan (correspondence)
    Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification
    Neural Information Processing Systems (NeurIPS), 2023
  • L. Shi, Z. Shen (本科生), Junchi Yan (correspondence)
    Double-Bounded Optimal Transport for Advanced Clustering and Classification.    
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
  • L. Shi, G. Zhang (本科生), H. Zhen (本科生), J. Fan, Junchi Yan (correspondence).
    Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective.    
    International Conference on Machine Learning (ICML), 2023.
  • S. Zhang, Q. Zhou, Z. Wang, F. Wang, Junchi Yan (correspondence).
    Patch-level Contrastive Learning via Positional Query for Visual Pre-training.    
    International Conference on Machine Learning (ICML), 2023.
  • S. Zhang, F. Zhu, R. Zhao, Junchi Yan (correspondence)
    Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning.    
    International Conference on Learning Representations (ICLR), 2023.
  • S. Zhang, F. Zhu, R. Zhao, Junchi Yan (correspondence)
    Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining.    
    International Conference on Learning Representations (ICLR), 2023.
  • S. Zhang, L. Qiu, F. Zhu, Junchi Yan (correspondence), H. Zhang, R. Zhao, H. Li, X. Yang,
    Align Representations with Base: A New Approach to Self-Supervised Learning.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  • S. Zhang, F. Zhu, Junchi Yan (correspondence), R. Zhao, X. Yang
    Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning
    International Conference on Learning Representations (ICLR), 2022.
  • X. Yang, Y. Wang, J. Sun, X. Zhang, S. Zhang, Z. Li, Junchi Yan (correspondence)
    Nonlinear ICA Using Volume-Preserving Transformations.  
    International Conference on Learning Representations (ICLR), 2022.
  • H. Zhang, Q. Wu, Junchi Yan, D. Wipf, P. S. Yu,
    From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.
    Neural Information Processing Systems (NeurIPS), 2021
  • S. Zhang, M. Liu, Junchi Yan (correspondence), H. Zhang, L. Huang, P. Lu, X. Yang.
    m-mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2022.
  • X. Yang, C. Deng, Z. Dang, K. Wei, Junchi Yan,
    SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network.    
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  • Y. Li (本科生), Y. Mo (本科生), L. Shi, Junchi Yan (correspondence).
    Improving Generative Adversarial Networks via Adversarial Learning in Latent Space .
    Neural Information Processing Systems (NeurIPS) (spotlight), 2022
  • Y. Li, L. Shi, Junchi Yan
    IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2023.
  • X. Yang, Junchi Yan (correspondence), Y. Cheng, Y. Zhang.
    Learning Deep Generative Clustering via Mutual Information Maximization  
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(9): 6263-6275, 2023
Network Embedding:
Students (alumni): Hao Xiong and Xinbo Du et al.
  • H. Xiong, Junchi Yan (correspondence), Z. Huang.
    Learning Regularized Noise Contrastive Estimation for Robust Network Embedding.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 35(5): 5017-5034, 2023
  • X. Du, Junchi Yan (correspondence), R. Zhang, H. Zha.
    Cross-network Skip-gram Embedding for Joint Network Alignment and Link Prediction.    
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2022, 34(3): 1080-1095.
    code
  • H. Xiong, Junchi Yan (correspondence).
    BTWalk: Branching Tree Random Walk for Multi-order Structured Network Embedding.  
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2022, 34(8): 3611 - 3628.
  • H. Xiong, Junchi Yan (correspondence), L. Pan.
    Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment.    
    Knowledge Discovery and Data Mining Conference (SIGKDD), 2021.
  • X. Du, Junchi Yan (correspondence), H. Zha.
    Joint Link Prediction and Network Alignment via Cross-graph Embedding.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2019.
    dataset
  • S. Yang, J. Tian, H. Zhang, Junchi Yan (correspondence), H. He, Y. Jin.
    TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics.    
    International Joint Conferences on Artificial Intelligence (IJCAI), 2019.
U.S. Patent
|| Generating a clustering model and clustering based on the clustering model, US Patent App. 14/812141
|| Biased users detection, US Patent App. 14/886426
|| Predicting waiting passenger count and evaluation, US Patent App. 14/722879
|| Analyzing property of protein sequence, US Patent App. 14/669748
|| Identifying subsurface material layer, US Patent App. 14/527272
|| Warping sequence data for learning in neural networks, US Patent App. 16/184180
|| Self-guided object detection in regular images, US10956796
|| Neural network training, US10839226
|| Privacy-preserving smart metering, US11176624
|| Determining an object referenced within informal online communications, US10296527
|| Training a machine learning model in a distributed privacy-preserving environment, US11443226
|| Product defect detection, US11756185
|| Model based data processing, US010832162
|| Training a self-driving vehicle, US010752239
|| Self-similarity analysis for defect detection on patterned industrial objects, US10726540
|| Interest highlight and recommendation based on interaction in long text reading, US10691893
|| Genome compression and decompression, US10679727
|| Multi-layer information fusing for prediction, US10679143
|| Processing GPS drifting, US10605925
|| Managing gene sequences, US10586609
|| Object detection, US10559078/10706530
|| Object classification based on decoupling a background from a foreground of an image, US10692220
|| Optimizing tables with too many columns in a database, US10331639
|| Method and apparatus of data classification for routes in a digitized map, US10436589/10429189/11860911
|| Expense compliance checking based on trajectory detection, US10510125
|| Fast joint template machining, US10346716
|| Providing computation services with privacy, US10333715
|| Quality evaluation, US10424059
|| Data cube generation, US9965503
|| Managing task in mobile device, US9722947
|| Location based on call detail record, US9723441
|| Work-piece defect inspection via optical images and ct images, US9721334
|| Method and apparatus for evaluating predictive model, US9684634
|| Method and apparatus for generating data in a missing segment of a time data sequence, US9684872
|| Creating a software performance testing environment on a virtual machine system, US9921939/US9921940
|| Foreign organization name matching, US9659086
|| Building missing movement path of an object, US9644976
|| Pattern recognition based on information integration, US9355332
|| Method and apparatus of determining air quality, US9740967, US9317732
|| Handing off a terminal among wireless access points, US9532285
|| Generating a training model based on feedback, US9659258/10346746
|| Monitoring interesting subjects, US9584608
|| Method and apparatus of estimating wave velocity of negative pressure wave in a fluid transportation pipeline, US9534979