Partial List of Publications (by both graduates and undergraduates)
Machine Learning for Graph and Combinatorial Optimization:
Students (alumni): Runzhong Wang, Chang Liu, Yang Li, Haoyu Geng, Han Lu, Yixuan He (Oxford), et al.
- J. Ma, X. Jiang, A. Fan, J. Jiang, Junchi Yan (correspondence)
Image Matching from Handcrafted to Deep Features: A Survey.   
International Journal of Computer Vision (IJCV) 129, 23–79, 2021R. Wang, Junchi Yan (correspondence), X. Yang
Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.   
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023, 45(6): 6984-7000 [project page]R. Wang, Junchi Yan (correspondence), X. Yang
Unsupervised Learning of Graph Matching with Mixture of Modes via Discrepancy Minimization.   
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023, DOI: 10.1109/TPAMI.2023.3257830 [project page]R. Wang, Junchi Yan (correspondence), X. Yang
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching.   
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022, 44(9): 5261-5279 [arxiv] [project page]Y. Jin, Q. Bao, R. Wang, Junchi Yan, Kun He.
CoNet: Complementary Encoding Networks for Solving Combinatorial Problems on Graph.    
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.Q. Ren, Q. Bao, R. Wang, Junchi Yan (correspondence).
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond.    
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.R. Wang, H. Hua, G. Liu, J. Zhang, Junchi Yan (correspondence), F. Qi, S. Yang, X. Yang
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs.
Neural Information Processing Systems (NeurIPS), 2021R. Wang, Junchi Yan (correspondence), X. Yang.
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning.
Neural Information Processing Systems (NeurIPS), 2020Y. He, Q. Gan, D. Wipf, G. Reinert, Junchi Yan , M. Cucuringu.
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks .   
International Conference on Machine Learning (ICML), 2022.X. Yang, C. Deng, K. Wei, Junchi Yan, W. Liu.
Adversarial Learning for Robust Deep Clustering.
Neural Information Processing Systems (NeurIPS), 2020G. Wang, Q. Zhen, Junchi Yan, L. Jiang.
Learning to Select Elements for Graphic Design.
ACM International Conference on Multimedia Retrieval (ICMR), 2020.Machine Learning for Science and Engineering (EDA, Drug, PDE, Neuroscience):Students (alumni): {EDA: Ruoyu Cheng, Yang Li, Xingbo Du, Ruizhe Zhong, Peiyu Wang, Jianyong Yuan et al.} & {Drug/Chem/Neuroscience: Nianzu Yang, Kaipeng Zeng, Huaijin Wu et al.} & {PDE: Mingquan Feng, Zelin Zhao et al.}- R. Chen, Junchi Yan (correspondence).
On Joint Learning for Solving Placement and Routing in Chip Design.
Neural Information Processing Systems (NeurIPS), 2021 - R. Chen, 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 - N. Yang, K. Zeng, 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, Q. Wu, Junchi Yan (correspondence)
MoleRec: Enhancing Medication Combination Recommendation with Substructure-Aware Molecule Learning.
The ACM Web Conference (WWW), 2023
Quantum Machine Learning (especially for Graphs):Students (alumni): Ge Yan, Xinyu Ye, Yehui Tang, Wenjie Wu, Xudong Lu et al.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.- Q. Wu, H. Zhang, Junchi Yan (correspondence), D. Wipf
Handling Distribution Shifts on Graphs: An Invariance Perspective    
International Conference on Learning Representation (ICLR), 2022. - Q. Wu, C. Yang, Junchi Yan (correspondence).
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach.
Neural Information Processing Systems (NeurIPS), 2021 - Q. Wu, W. Zhao, Z. Li, D. Wipf, Junchi Yan (correspondence).
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification.
Neural Information Processing Systems (NeurIPS) (spotlight), 2022 - Z. Li, Q. Wu, F. Nie, Junchi Yan (correspondence).
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs.
Neural Information Processing Systems (NeurIPS), 2022
Self-supervised (mainly Contastive) Learning:Students (alumni): Shaofeng Zhang, Liangliang Shi, Xiaojiang Yang et al.- 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 Representation (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 Representation (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
Discrete Time Space (Time Series) Learning:Students (alumni): Yunhao Zhang, Longyuan Li et al.- 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 - 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
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 - 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. - 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.
Autonomous Driving and Robotics:Students (alumni): Xiaosong Jia, Penghao Wu, Shengchao Hu, Xiaolei Chen, Zhe Ren et al.- S. Hu, L. Chen, P. Wu, H. Li, Junchi Yan, D. Tao.
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning .  
European Conference on Computer Vision (ECCV), 2022. - L. Chen, C. Sima, Y. Li, Z. Zheng, J. Xu, X. Geng, H. Li, C. He, J. Shi, Y. Qiao, Junchi Yan
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark.  
European Conference on Computer Vision (ECCV), 2022. - X. Jia, L. Chen, P. Wu, J. Zeng, Junchi Yan (correspondence), H. Li, Y. Qiao
Towards Capturing the Temporal Dynamics for Trajectory Prediction: a Coarse-to-Fine Approach .
Conference on Robotic Learning (CoRL), 2022 - P. Wu, X. Jia, L. Chen, Junchi Yan (correspondence), H. Li, Y. Qiao
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline .
Neural Information Processing Systems (NeurIPS), 2022 - Q. Zhang, Y. Li, Y. Luo, W. Shou, M. Foshey, Junchi Yan, J. Tenenbaum, W. Matusik, A. Torralba
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.
International Conference on Intelligent Robots and Systems (IROS), 2021 - L. Liu, Y. Chen, Junchi Yan (correspondence), Y. Zheng,
Optimal LED Spectral Multiplexing for NIR2RGB Translation.    
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Automatic Neural Architecture Search and Optimization:Students (alumni): Xiaoxing Wang, Zhexi Zhang et al.- X. Wang, W. Guo, J. Su, X. Yang, Junchi Yan (correspondence)
ZARTS: On Zero-order Optimization for Neural Architecture Search.
Neural Information Processing Systems (NeurIPS), 2022 - 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.
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 - 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
code - 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
Z. Jiang, T. Wang, 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): 3648-3663Visual (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年 [pdf], 2023 - X. Yang, Junchi Yan (correspondence)
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited.   
International Journal of Computer Vision (IJCV) 130, 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, 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
Robust and Trustable AI:Students (alumni): Ruoxi Chen, Haoxuan Wang, Yiting Chen, Ning Liao, Yucheng Luo, Huaqing Shao et al.- 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
Recommender Systems and Collaborative Filtering:Students (alumni): Chao Chen, Qitian Wu, et al.Machine Learning Basics and Miscellaneous:- 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. - 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.
Multi-agent System and Distributed Optimization:Students (alumni): Wenhao Li (ECNU) et al.- 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) 2023 [arxiv]
Generative Models:Students (alumni): Yang Li, Liangliang Shi and Xiaojiang Yang et al.- Y. Li, Y. Mo, L. Shi, Junchi Yan (correspondence).
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space .
Neural Information Processing Systems (NeurIPS) (spotlight), 2022 - 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), 2021.
Network Embedding:Students (alumni): Hao Xiong and Xinbo Du et al.- 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. - 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
U.S. Patent|| Determining an object referenced within informal online communications, US Patent App. 14/962232|| 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, US Patent App. 16/157418|| 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|| 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|| 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