default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKun Zhang 0001
Person information
- affiliation: Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation (PhD 2005): Chinese University of Hong Kong, Hong Kong
Other persons with the same name
- Kun Zhang — disambiguation page
- Kun Zhang 0002
![[0000-0002-3543-0137]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-3543-0137")
— Taiyuan University of Technology, College of Mechanical Engineering, China - Kun Zhang 0003 — East China Normal University, Key Laboratory of Geographic Information Science, Shanghai, China
- Kun Zhang 0004 — State University of New York at Stony Brook, Department of Physics and Astronomy, NY, USA
- Kun Zhang 0005 — Northeastern University, College of Information Science and Engineering, Shenyang, China
- Kun Zhang 0006 — Georgia Institute of Technology, Atlanta, GA, USA
- Kun Zhang 0007 — Beijing University of Technology, Key Laboratory of Advanced Manufacturing Technology, China
- Kun Zhang 0008 — Air Force Engineering University, Information and Navigation College, Xian, China
- Kun Zhang 0009 — Xidian University, National Laboratory of Radar Signal Processing, China
- Kun Zhang 0010 — Nantong University, School of Electrical Engineering, China
- Kun Zhang 0011 — Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou, China
- Kun Zhang 0012 — Xavier University of Louisiana, New Orleans, LA, USA
- Kun Zhang 0013 — Shandong University, Jinan, China (and 1 more)
- Kun Zhang 0014 — University of Colorado at Boulder, Boulder, CO, USA
- Kun Zhang 0015 — Hefei University of Technology, School of Computer Science and Information Engineering, Key Laboratory of Knowledge Engineering with Big Data, Hefei, China (and 1 more)
- Kun Zhang 0016 — University of Chinese Academy of Sciences, School of Cyber Security, Beijing, China
- Kun Zhang 0017 — Hong Kong University of Science and Technology, Hong Kong (and 1 more)
- Kun Zhang 0018 — Northwestern Polytechnical University, School of Astronautics, Shaanxi Aerospace Flight Vehicle Design Key Laboratory, Xi'an, China
- Kun Zhang 0019 — Southeast University, School of Economics and Management, Nanjing, China
- Kun Zhang 0020 — University of California San Diego, Department of Bioengineering, La Jolla, CA, USA
- Kun Zhang 0021 — Nanjing University of Science and Technology, School of Computer Science and Engineering, China
- Kun Zhang 0022 — Shenyang University of Technology, School of Materials Science and Engineering, China
- Kun Zhang 0023 — Shaanxi Provincial Tumor Hospital, Xi'an, China
- Kun Zhang 0024 — Chinese Academy of Sciences, Institute of Tibetan Plateau Research, Beijing, China (and 1 more)
- Kun Zhang 0025 — University of Science and Technology of China, CAS Key Laboratory of Wireless-Optical Communications, Hefei, China
- Kun Zhang 0026 — Shandong University of Science and Technology, College of Mechanical and Electronic Engineering, Qingdao, China (and 2 more)
- Kun Zhang 0027 — Huaqiao University, College of Tourism, Quanzhou, China
- Kun Zhang 0028 — University of Science and Technology of China, Department of Automation, Hefei, China
- Kun Zhang 0029 — Northwestern Polytechnical University, School of Electronics and Information, Xi'an, China
- Kun Zhang 0030 — Beihang University, Fert Beijing Research Institute, Beijing, China (and 2 more)
- Kun Zhang 0031 — Central China Normal University, National Engineering Laboratory for Educational Big Data and the National Engineering Research Center for E-Learning, Wuhan, China
- Kun Zhang 0032 — Luoyang Polytechnic, School of Automotive and Rail Transportation, Luoyang, China
- Kun Zhang 0033 — South China University of Technology, School of Electronics and Information Engineering, Guangzhou, China
- Kun Zhang 0034 — Xi'an Peihua University, School of Communication, Xi'an, China
- Kun Zhang 0035 — Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, China
- Kun Zhang 0036 — Renmin University of China, Institute of Statistics and Big Data, Beijing, China (and 1 more)
- Kun Zhang 0037 — Zhejiang Lab, Hangzhou, China
- Kun Zhang 0038 — Shandong University of Science and Technology, College of Intelligent Equipment, Tai'an, China
- Kun Zhang 0039 — Liaoning University, School of Mathematics, Shenyang, China
- Kun Zhang 0040 — University of Science and Technology of China, School of Information Science and Technology, Hefei, China (and 1 more)
- Kun Zhang 0041 — Chinese Academy of Sciences, Data Intelligence System Research Center, Institute of Computing Technology, China (and 1 more)
- Kun Zhang 0042 — Chinese Academy of Sciences, Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Hefei, China (and 1 more)
- Kun Zhang 0043 — Google Inc., Google Health, Mountain View, CA, USA
- Kun Zhang 0044 — University of Electronic Science and Technology of China, School of Mechanical and Electrical Engineering, Chengdu, China
- Kun Zhang 0045 — INRIA Saclay - île-de-France, Palaiseau, France (and 2 more)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Books and Theses
- 2005
[b1]Kun Zhang:
Extensions of independent component analysis: towards applications. Chinese University of Hong Kong, Hong Kong, 2005
Journal Articles
- 2025
- [j47]Zhengmao Zhu, Hong-Long Tian, Xionghui Chen, Kun Zhang, Yang Yu:
Offline model-based reinforcement learning with causal structured world models. Frontiers Comput. Sci. 19(4): 194347 (2025) - [j46]Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu:
Alignclip: navigating the misalignments for robust vision-language generalization. Mach. Learn. 114(3): 58 (2025) - [j45]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. Trans. Mach. Learn. Res. 2025 (2025) - [j44]Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, Lu Cheng:
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. Trans. Mach. Learn. Res. 2025 (2025) - [j43]Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang:
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. IEEE Trans. Multim. 27: 1142-1152 (2025) - 2024
- [j42]Yue Cong, Jing Qiu, Kun Zhang, Zhongyang Fang, Chengliang Gao, Shen Su, Zhihong Tian:
Ada-FFL: Adaptive computing fairness federated learning. CAAI Trans. Intell. Technol. 9(3): 573-584 (2024) - [j41]Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He:
Hierarchical damage correlations for old photo restoration. Inf. Fusion 107: 102340 (2024) - [j40]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. J. Mach. Learn. Res. 25: 60:1-60:8 (2024) - [j39]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. J. Mach. Learn. Res. 25: 154:1-154:50 (2024) - [j38]Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang:
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. J. Mach. Learn. Res. 25: 191:1-191:61 (2024) - [j37]Zijian Li, Ruichu Cai, Tom Z. J. Fu, Zhifeng Hao, Kun Zhang:
Transferable Time-Series Forecasting Under Causal Conditional Shift. IEEE Trans. Pattern Anal. Mach. Intell. 46(4): 1932-1949 (2024) - [j36]Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang:
Graph Domain Adaptation: A Generative View. ACM Trans. Knowl. Discov. Data 18(3): 60:1-60:24 (2024) - [j35]Hanqi Yan, Lin Gui, Menghan Wang, Kun Zhang, Yulan He:
Explainable Recommender With Geometric Information Bottleneck. IEEE Trans. Knowl. Data Eng. 36(7): 3036-3046 (2024) - [j34]Weiwei Cai, Huaidong Zhang, Xuemiao Xu, Chenshu Xu, Kun Zhang, Shengfeng He:
Delving Into Important Samples of Semi-Supervised Old Photo Restoration: A New Dataset and Method. IEEE Trans. Multim. 26: 9866-9879 (2024) - [j33]Kun Zhang, Ilya Shpitser, Sara Magliacane, Davide Bacciu, Fei Wu, Changshui Zhang, Peter Spirtes:
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4899-4901 (2024) - [j32]Cheng Xu, Keke Li, Xuandi Luo, Xuemiao Xu, Shengfeng He, Kun Zhang:
Fully Deformable Network for Multiview Face Image Synthesis. IEEE Trans. Neural Networks Learn. Syst. 35(7): 8854-8868 (2024) - 2023
- [j31]Zeyu Tang, Jiji Zhang, Kun Zhang:
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective. ACM Comput. Surv. 55(13s): 299:1-299:37 (2023) - [j30]Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions. Neurocomputing 526: 48-61 (2023) - [j29]Negar Kiyavash, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak:
Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory 4: iv (2023) - [j28]Weiwei Cai, Huaidong Zhang, Xuemiao Xu, Shengfeng He, Kun Zhang, Jing Qin:
Contextual-Assisted Scratched Photo Restoration. IEEE Trans. Circuits Syst. Video Technol. 33(10): 5458-5469 (2023) - [j27]Hao Zhang, Yewei Xia, Kun Zhang, Shuigeng Zhou, Jihong Guan:
Conditional Independence Test Based on Residual Similarity. ACM Trans. Knowl. Discov. Data 17(8): 117:1-117:18 (2023) - [j26]Yige Zhang, Weixiong Rao, Kun Zhang, Lei Chen:
Outdoor Position Recovery From Heterogeneous Telco Cellular Data. IEEE Trans. Knowl. Data Eng. 35(11): 11736-11750 (2023) - [j25]Yuewen Sun, Kun Zhang, Changyin Sun:
Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations. IEEE Trans. Neural Networks Learn. Syst. 34(2): 1035-1048 (2023) - 2022
- [j24]Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang:
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models. Entropy 24(4): 512 (2022) - [j23]Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang:
Relevance attack on detectors. Pattern Recognit. 124: 108491 (2022) - [j22]Wei Chen, Ruichu Cai, Kun Zhang, Zhifeng Hao:
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders. IEEE Trans. Neural Networks Learn. Syst. 33(7): 2816-2827 (2022) - 2021
- [j21]Mingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, George C. Tseng, Kun Zhang, Kayhan Batmanghelich:
Unpaired data empowers association tests. Bioinform. 37(6): 785-792 (2021) - [j20]M. Reza Heydari, Saber Salehkaleybar, Kun Zhang:
Adversarial orthogonal regression: Two non-linear regressions for causal inference. Neural Networks 143: 66-73 (2021) - [j19]Jie Qiao, Ruichu Cai, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models. ACM Trans. Intell. Syst. Technol. 12(6): 80:1-80:28 (2021) - [j18]Yige Zhang, Aaron Yi Ding, Jörg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao:
Transfer Learning-Based Outdoor Position Recovery With Cellular Data. IEEE Trans. Mob. Comput. 20(5): 2094-2110 (2021) - 2020
- [j17]Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang:
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. J. Mach. Learn. Res. 21: 39:1-39:24 (2020) - [j16]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020) - [j15]Xubo Fu, Kun Zhang, Changgang Wang, Chao Fan:
Multiple player tracking in basketball court videos. J. Real Time Image Process. 17(6): 1811-1828 (2020) - 2019
- [j14]Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui:
Introduction to the Special Section on Advances in Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 10(5): 45:1-45:3 (2019) - 2018
- [j13]Lin Liu, Jiuyong Li, Kun Zhang, Emre Kiciman, Negar Kiyavash:
Guest Editorial: Special Issue on Causal Discovery 2017. Int. J. Data Sci. Anal. 6(1): 1-2 (2018) - 2017
- [j12]Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu:
Guest editorial: special issue on causal discovery. Int. J. Data Sci. Anal. 3(2): 79-80 (2017) - 2016
- [j11]Kun Zhang, Zhikun Wang, Jiji Zhang, Bernhard Schölkopf:
On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model. ACM Trans. Intell. Syst. Technol. 7(2): 13:1-13:22 (2016) - [j10]Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl:
Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 7(2): 17:1-17:3 (2016) - 2014
- [j9]Zhitang Chen, Kun Zhang, Laiwan Chan, Bernhard Schölkopf:
Causal Discovery via Reproducing Kernel Hilbert Space Embeddings. Neural Comput. 26(7): 1484-1517 (2014) - 2012
- [j8]Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf:
Information-geometric approach to inferring causal directions. Artif. Intell. 182-183: 1-31 (2012) - 2010
- [j7]Kun Zhang, Lai-Wan Chan:
Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion. Neurocomputing 73(13-15): 2580-2588 (2010) - [j6]Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer:
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. 11: 1709-1731 (2010) - 2007
- [j5]Kun Zhang, Laiwan Chan:
Separating Convolutive Mixtures By Pairwise Mutual Information Minimization. IEEE Signal Process. Lett. 14(12): 992-995 (2007) - 2006
- [j4]Kun Zhang, Lai-Wan Chan:
An Adaptive Method for Subband Decomposition ICA. Neural Comput. 18(1): 191-223 (2006) - [j3]Wan Zhang, Liu Wenyin, Kun Zhang:
Symbol Recognition with Kernel Density Matching. IEEE Trans. Pattern Anal. Mach. Intell. 28(12): 2020-2024 (2006) - [j2]Kun Zhang, Lai-Wan Chan:
Dimension reduction as a deflation method in ICA. IEEE Signal Process. Lett. 13(1): 45-48 (2006) - 2005
- [j1]Kun Zhang, Lai-Wan Chan:
Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures. Neural Comput. 17(2): 425-452 (2005)
Conference and Workshop Papers
- 2025
- [c205]Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang:
Nonparametric Factor Analysis and Beyond. AISTATS 2025: 424-432 - [c204]Jiaqi Sun, Yujia Zheng, Xinshuai Dong, Haoyue Dai, Kun Zhang:
Type Information-Assisted Self-Supervised Knowledge Graph Denoising. AISTATS 2025: 964-972 - [c203]Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang:
Causal Representation Learning from General Environments under Nonparametric Mixing. AISTATS 2025: 3700-3708 - [c202]Shaoan Xie, Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang:
SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. CVPR 2025: 29780-29790 - [c201]Minghao Fu, Danning Li, Aryan Gadhiya, Benjamin Lambright, Mohamed Alowais, Mohab Bahnassy, Saad El Dine Elletter, Hawau Olamide Toyin, Haiyan Jiang, Kun Zhang, Hanan Aldarmaki:
Infant Cry Detection Using Causal Temporal Representation. ICASSP 2025: 1-5 - [c200]Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang:
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning. ICLR 2025 - [c199]Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Guangyi Chen, Kun Zhang:
On the Identification of Temporal Causal Representation with Instantaneous Dependence. ICLR 2025 - [c198]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Mingming Gong, Biwei Huang, Kun Zhang, Anton van den Hengel, Javen Qinfeng Shi:
Analytic DAG Constraints for Differentiable DAG Learning. ICLR 2025 - [c197]Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han:
Noisy Test-Time Adaptation in Vision-Language Models. ICLR 2025 - [c196]Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang:
When Selection Meets Intervention: Additional Complexities in Causal Discovery. ICLR 2025 - [c195]Yuke Li, Yujia Zheng, Guangyi Chen, Kun Zhang, Heng Huang:
Identification of Intermittent Temporal Latent Process. ICLR 2025 - [c194]Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu:
Prompting Fairness: Integrating Causality to Debias Large Language Models. ICLR 2025 - [c193]Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yian Ma, Biwei Huang:
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. ICLR 2025 - [c192]Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, Tongliang Liu:
Flow: Modularized Agentic Workflow Automation. ICLR 2025 - [c191]Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang:
Differentiable Causal Discovery for Latent Hierarchical Causal Models. ICLR 2025 - [c190]Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang:
Causal Representation Learning from Multimodal Biomedical Observations. ICLR 2025 - [c189]Anpeng Wu, Haiyi Qiu, Zhengming Chen, Zijian Li, Ruoxuan Xiong, Fei Wu, Kun Zhang:
Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference. ICLR 2025 - [c188]Tianjun Yao, Yongqiang Chen, Kai Hu, Tongliang Liu, Kun Zhang, Zhiqiang Shen:
Learning Graph Invariance by Harnessing Spuriosity. ICLR 2025 - [c187]Yu Yao, Yang Zhou, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu:
A Robust Method to Discover Causal or Anticausal Relation. ICLR 2025 - [c186]Anpeng Wu, Haoxuan Li, Chunyuan Zheng, Kun Kuang, Kun Zhang:
Classifying Treatment Responders: Bounds and Algorithms. KDD (1) 2025: 1611-1622 - [c185]Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge. UMAP 2025: 83-93 - [c184]Ruichu Cai, Zhifan Jiang, Kaitao Zheng, Zijian Li, Weilin Chen, Xuexin Chen, Yifan Shen, Guangyi Chen, Zhifeng Hao, Kun Zhang:
Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. WWW 2025: 3247-3266 - 2024
- [c183]Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman H. Khan, Kun Zhang, Fahad Khan:
S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment. AAAI 2024: 7278-7286 - [c182]Yuewen Sun, Erli Wang, Biwei Huang, Chaochao Lu, Lu Feng, Changyin Sun, Kun Zhang:
ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation. AAAI 2024: 15193-15201 - [c181]Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang:
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants. AAAI 2024: 20353-20361 - [c180]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. AISTATS 2024: 154-162 - [c179]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR 2024: 71-105 - [c178]Umaima Rahman, Guangyi Chen, Kun Zhang:
MRIShift: Disentangled Representation Learning for 3D MRI Lesion Segmentation Under Distributional Shifts. EUVIP 2024: 1-6 - [c177]Yuke Li, Lixiong Chen, Guangyi Chen, Ching-Yao Chan, Kun Zhang, Stefano Anzellotti, Donglai Wei:
Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction. HCMA@MM 2024: 55-64 - [c176]Guangyi Chen, Yuke Li, Xiao Liu, Zijian Li, Eman Al Suradi, Donglai Wei, Kun Zhang:
LLCP: Learning Latent Causal Processes for Reasoning-based Video Question Answer. ICLR 2024 - [c175]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. ICLR 2024 - [c174]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. ICLR 2024 - [c173]Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang:
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability. ICLR 2024 - [c172]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c171]Xiu-Chuan Li, Kun Zhang, Tongliang Liu:
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. ICLR 2024 - [c170]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Identifiable Latent Polynomial Causal Models through the Lens of Change. ICLR 2024 - [c169]Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. ICLR 2024 - [c168]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. ICML 2024 - [c167]Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang:
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process. ICML 2024 - [c166]Shunxing Fan, Mingming Gong, Kun Zhang:
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. ICML 2024 - [c165]Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang:
Score-Based Causal Discovery of Latent Variable Causal Models. ICML 2024 - [c164]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. ICML 2024 - [c163]Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang:
Empowering Graph Invariance Learning with Deep Spurious Infomax. ICML 2024 - [c162]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. ICML 2024 - [c161]Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang:
Learning Discrete Latent Variable Structures with Tensor Rank Conditions. NeurIPS 2024 - [c160]Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang:
Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. NeurIPS 2024 - [c159]Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
On the Parameter Identifiability of Partially Observed Linear Causal Models. NeurIPS 2024 - [c158]Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang:
Natural Counterfactuals With Necessary Backtracking. NeurIPS 2024 - [c157]Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang:
Learning Discrete Concepts in Latent Hierarchical Models. NeurIPS 2024 - [c156]Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang:
Towards Understanding Extrapolation: a Causal Lens. NeurIPS 2024 - [c155]Loka Li, Haoyue Dai, Hanin Al Ghothani, Biwei Huang, Jiji Zhang, Shahar Harel, Isaac Bentwich, Guangyi Chen, Kun Zhang:
On Causal Discovery in the Presence of Deterministic Relations. NeurIPS 2024 - [c154]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. NeurIPS 2024 - [c153]Yiwen Qiu, Yujia Zheng, Kun Zhang:
Identifying Selections for Unsupervised Subtask Discovery. NeurIPS 2024 - [c152]Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang:
Causal Temporal Representation Learning with Nonstationary Sparse Transition. NeurIPS 2024 - [c151]Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning. NeurIPS 2024 - [c150]Donghuo Zeng, Roberto Sebastian Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. PERSUASIVE 2024: 287-300 - [c149]Tianjun Yao, Jiaqi Sun, Defu Cao, Kun Zhang, Guangyi Chen:
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification. WWW 2024: 709-720 - 2023
- [c148]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CLeaR 2023: 726-751 - [c147]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CLeaR 2023: 752-771 - [c146]Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang:
Understanding Masked Autoencoders via Hierarchical Latent Variable Models. CVPR 2023: 7918-7928 - [c145]Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang:
Unpaired Image-to-Image Translation with Shortest Path Regularization. CVPR 2023: 10177-10187 - [c144]Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang:
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction. CVPR 2023: 17874-17884 - [c143]Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang:
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model. CVPR 2023: 22428-22437 - [c142]Guangyi Chen, Xiao Liu, Guangrun Wang, Kun Zhang, Philip H. S. Torr, Xiao-Ping Zhang, Yansong Tang:
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer. ICCV 2023: 13899-13909 - [c141]Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang:
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. ICLR 2023 - [c140]Yewen Fan, Nian Si, Kun Zhang:
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. ICLR 2023 - [c139]Junlong Li, Guangyi Chen, Yansong Tang, Jinan Bao, Kun Zhang, Jie Zhou, Jiwen Lu:
GAIN: On the Generalization of Instructional Action Understanding. ICLR 2023 - [c138]Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang:
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. ICLR 2023 - [c137]Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang:
Causal Balancing for Domain Generalization. ICLR 2023 - [c136]Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang:
Multi-domain image generation and translation with identifiability guarantees. ICLR 2023 - [c135]Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang:
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. ICLR 2023 - [c134]Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang:
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. ICML 2023: 3380-3407 - [c133]Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. ICML 2023: 4611-4622 - [c132]Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu:
Model Transferability with Responsive Decision Subjects. ICML 2023: 4921-4952 - [c131]Yang Liu, Hao Cheng, Kun Zhang:
Identifiability of Label Noise Transition Matrix. ICML 2023: 21475-21496 - [c130]Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang:
Feature Expansion for Graph Neural Networks. ICML 2023: 33156-33176 - [c129]Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu:
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023: 39660-39673 - [c128]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis. ISBI 2023: 1-5 - [c127]Tianjun Yao, Yingxu Wang, Kun Zhang, Shangsong Liang:
Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information. KDD 2023: 3070-3081 - [c126]Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang:
Subspace Identification for Multi-Source Domain Adaptation. NeurIPS 2023 - [c125]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. NeurIPS 2023 - [c124]Yuren Liu, Biwei Huang, Zhengmao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang:
Learning World Models with Identifiable Factorization. NeurIPS 2023 - [c123]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. NeurIPS 2023 - [c122]Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang:
Temporally Disentangled Representation Learning under Unknown Nonstationarity. NeurIPS 2023 - [c121]Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang:
Counterfactual Generation with Identifiability Guarantees. NeurIPS 2023 - [c120]Yujia Zheng, Kun Zhang:
Generalizing Nonlinear ICA Beyond Structural Sparsity. NeurIPS 2023 - 2022
- [c119]Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan:
Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. AAAI 2022: 5942-5949 - [c118]Zhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang, Ruichu Cai:
Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI 2022: 6350-6357 - [c117]Zheng-Mao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang:
Invariant Action Effect Model for Reinforcement Learning. AAAI 2022: 9260-9268 - [c116]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. AISTATS 2022: 8095-8111 - [c115]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang:
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS 2022: 8176-8198 - [c114]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). IEEE Big Data 2022: 4995-4996 - [c113]Zeyu Tang, Kun Zhang:
Attainability and Optimality: The Equalized Odds Fairness Revisited. CLeaR 2022: 754-786 - [c112]Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao:
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. CVPR 2022: 18228-18238 - [c111]Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich:
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. CVPR 2022: 18290-18299 - [c110]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR 2022 - [c109]Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning with Kernel. ICLR 2022 - [c108]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal Transport for Causal Discovery. ICLR 2022 - [c107]Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang:
Learning Temporally Causal Latent Processes from General Temporal Data. ICLR 2022 - [c106]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c105]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022: 9260-9279 - [c104]Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang:
Partial disentanglement for domain adaptation. ICML 2022: 11455-11472 - [c103]Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022: 24370-24387 - [c102]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi:
Truncated Matrix Power Iteration for Differentiable DAG Learning. NeurIPS 2022 - [c101]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. NeurIPS 2022 - [c100]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS 2022 - [c99]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [c98]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS 2022 - [c97]Shaoan Xie, Qirong Ho, Kun Zhang:
Unsupervised Image-to-Image Translation with Density Changing Regularization. NeurIPS 2022 - [c96]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. NeurIPS 2022 - [c95]Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. NeurIPS 2022 - [c94]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. NeurIPS 2022 - [c93]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - 2021
- [c92]Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu:
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI 2021: 643-650 - [c91]Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang:
Testing Independence Between Linear Combinations for Causal Discovery. AAAI 2021: 6538-6546 - [c90]Ni Y. Lu, Kun Zhang, Changhe Yuan:
Improving Causal Discovery By Optimal Bayesian Network Learning. AAAI 2021: 8741-8748 - [c89]Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang:
Unaligned Image-to-Image Translation by Learning to Reweight. ICCV 2021: 14154-14164 - [c88]Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang:
Progressive Open-Domain Response Generation with Multiple Controllable Attributes. IJCAI 2021: 3279-3285 - [c87]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c86]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NeurIPS 2021: 20308-20320 - [c85]Jeffrey Adams, Niels Hansen, Kun Zhang:
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. NeurIPS 2021: 22822-22833 - [c84]Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang:
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NeurIPS 2021: 24791-24803 - 2020
- [c83]Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du:
Compressed Self-Attention for Deep Metric Learning. AAAI 2020: 3561-3568 - [c82]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. AAAI 2020: 6526-6533 - [c81]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020: 10153-10161 - [c80]Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi:
Adaptive Task Sampling for Meta-learning. ECCV (18) 2020: 752-769 - [c79]AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang:
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML 2020: 3494-3504 - [c78]Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao:
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020: 3843-3853 - [c77]Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao:
Label-Noise Robust Domain Adaptation. ICML 2020: 10913-10924 - [c76]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2020: 1-3 - [c75]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS 2020 - [c74]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS 2020 - [c73]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. NeurIPS 2020 - [c72]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NeurIPS 2020 - [c71]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How do fair decisions fare in long-term qualification? NeurIPS 2020 - 2019
- [c70]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019: 3664-3671 - [c69]Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang:
Causal Discovery in the Presence of Missing Data. AISTATS 2019: 1762-1770 - [c68]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019: 3449-3458 - [c67]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019: 3487-3496 - [c66]Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng:
PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network. CIKM 2019: 1933-1942 - [c65]Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao:
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. CVPR 2019: 2427-2436 - [c64]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019: 2901-2910 - [c63]Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representations for Domain Adaptation. ICML 2019: 7523-7532 - [c62]Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019: 1609-1615 - [c61]Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao:
Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019: 2060-2066 - [c60]Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2019: 1-3 - [c59]Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxilary Classifiers GAN. NeurIPS 2019: 1328-1337 - [c58]Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao:
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. NeurIPS 2019: 6880-6890 - [c57]Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NeurIPS 2019: 12773-12784 - [c56]Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang:
Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019: 12863-12872 - [c55]Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour:
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NeurIPS 2019: 13510-13521 - [c54]Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019: 186-195 - [c53]Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan:
Domain Generalization via Multidomain Discriminant Analysis. UAI 2019: 292-302 - 2018
- [c52]Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang:
Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. AAAI 2018: 2516-2523 - [c51]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang:
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018: 4000-4007 - [c50]Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao:
Deep Domain Generalization via Conditional Invariant Adversarial Networks. ECCV (15) 2018: 647-663 - [c49]Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu:
Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2018: 1-3 - [c48]Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour:
Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560 - [c47]Yanwu Xu, Mingming Gong, Huan Fu, Dacheng Tao, Kun Zhang, Kayhan Batmanghelich:
Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation. BrainLes@MICCAI (2) 2018: 222-233 - [c46]Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery from Discrete Data using Hidden Compact Representation. NeurIPS 2018: 2671-2679 - [c45]AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang:
Multi-domain Causal Structure Learning in Linear Systems. NeurIPS 2018: 6269-6279 - [c44]Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang:
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. NeurIPS 2018: 6670-6679 - [c43]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018: 1063-1072 - 2017
- [c42]Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan:
Causal Discovery Using Regression-Based Conditional Independence Tests. AAAI 2017: 1250-1256 - [c41]Kun Zhang:
Causal Learning and Machine Learning. AMBN 2017: 4 - [c40]Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017: 913-918 - [c39]Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017: 1347-1353 - [c38]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. NIPS 2017: 3011-3021 - [c37]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao:
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 - 2016
- [c36]Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. ICML 2016: 2839-2848 - [c35]Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal:
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. UAI 2016 - [c34]Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour:
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 - 2015
- [c33]Kun Zhang, Mingming Gong, Bernhard Schölkopf:
Multi-Source Domain Adaptation: A Causal View. AAAI 2015: 3150-3157 - [c32]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Dacheng Tao, Philipp Geiger:
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015: 1898-1906 - [c31]Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing:
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015: 1917-1925 - [c30]Biwei Huang, Kun Zhang, Bernhard Schölkopf:
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015: 3561-3568 - 2014
- [c29]Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf:
A Permutation-Based Kernel Conditional Independence Test. UAI 2014: 132-141 - 2013
- [c28]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference 2013: 129-141 - [c27]Kun Zhang, Zhikun Wang, Bernhard Schölkopf:
On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example. ICDM Workshops 2013: 139-146 - [c26]Zhitang Chen, Kun Zhang, Laiwan Chan:
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. ICDM 2013: 1003-1008 - [c25]Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang:
Domain Adaptation under Target and Conditional Shift. ICML (3) 2013: 819-827 - 2012
- [c24]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. ICML 2012 - [c23]Zhitang Chen, Kun Zhang, Laiwan Chan:
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. NIPS 2012: 1736-1744 - 2011
- [c22]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011: 804-813 - [c21]Jakob Zscheischler, Dominik Janzing, Kun Zhang:
Testing whether linear equations are causal: A free probability theory approach. UAI 2011: 839-846 - [c20]Kun Zhang, Aapo Hyvärinen:
A General Linear Non-Gaussian State-Space Model. ACML 2011: 113-128 - 2010
- [c19]Min-Ling Zhang, Kun Zhang:
Multi-label learning by exploiting label dependency. KDD 2010: 999-1008 - [c18]Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf:
Probabilistic latent variable models for distinguishing between cause and effect. NIPS 2010: 1687-1695 - [c17]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. UAI 2010: 143-150 - [c16]Kun Zhang, Aapo Hyvärinen:
Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI 2010: 709-716 - [c15]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010: 717-724 - [c14]Kun Zhang, Aapo Hyvärinen:
Nonlinear acyclic causal models. NIPS Causality: Objectives and Assessment 2010: 157-164 - 2009
- [c13]Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen:
ICA with Sparse Connections: Revisited. ICA 2009: 195-202 - [c12]Kun Zhang, Aapo Hyvärinen:
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD (2) 2009: 570-585 - [c11]Kun Zhang, Aapo Hyvärinen:
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009: 647-655 - 2007
- [c10]Kun Zhang, Laiwan Chan:
Kernel-Based Nonlinear Independent Component Analysis. ICA 2007: 301-308 - [c9]Kun Zhang, Laiwan Chan:
Nonlinear independent component analysis with minimal nonlinear distortion. ICML 2007: 1127-1134 - [c8]Jian Li, Kun Zhang, Laiwan Chan:
Independent Factor Reinforcement Learning for Portfolio Management. IDEAL 2007: 1020-1031 - 2006
- [c7]Kun Zhang, Lai-Wan Chan:
ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments. ICA 2006: 311-318 - [c6]Kun Zhang, Lai-Wan Chan:
Enhancement of Source Independence for Blind Source Separation. ICA 2006: 731-738 - [c5]Kun Zhang, Lai-Wan Chan:
Extensions of ICA for Causality Discovery in the Hong Kong Stock Market. ICONIP (3) 2006: 400-409 - [c4]Kun Zhang, Lai-Wan Chan:
ICA with Sparse Connections. IDEAL 2006: 530-537 - 2005
- [c3]Kun Zhang, Lai-Wan Chan:
To apply score function difference based ICA algorithms to high-dimensional data. ESANN 2005: 291-296 - 2004
- [c2]Kun Zhang, Lai-Wan Chan:
Practical method for blind inversion of Wiener systems. IJCNN 2004: 2163-2168 - 2003
- [c1]Kun Zhang, Lai-Wan Chan:
Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA. ICANN 2003: 132-139
Editorship
- 2024
- [e6]Elena Baralis, Kun Zhang, Ernesto Damiani, Mérouane Debbah, Panos Kalnis, Xindong Wu:
IEEE International Conference on Data Mining, ICDM 2024, Abu Dhabi, United Arab Emirates, December 9-12, 2024. IEEE 2024, ISBN 979-8-3315-0668-1 [contents] - 2022
- [e5]Bernhard Schölkopf, Caroline Uhler, Kun Zhang:
1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11-13 April, 2022. Proceedings of Machine Learning Research 177, PMLR 2022 [contents] - [e4]James Cussens, Kun Zhang:
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands. Proceedings of Machine Learning Research 180, PMLR 2022 [contents] - 2020
- [e3]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. Proceedings of Machine Learning Research 127, PMLR 2020 [contents] - 2019
- [e2]Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2019, Anchorage, Alaska, USA, August 5, 2019. Proceedings of Machine Learning Research 104, PMLR 2019 [contents] - 2018
- [e1]Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu:
Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018, London, UK, 20 August 2018. Proceedings of Machine Learning Research 92, PMLR 2018 [contents]
Informal and Other Publications
- 2025
- [i198]Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, Tongliang Liu:
Flow: A Modular Approach to Automated Agentic Workflow Generation. CoRR abs/2501.07834 (2025) - [i197]Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang:
Towards Understanding Extrapolation: a Causal Lens. CoRR abs/2501.09163 (2025) - [i196]Gongxu Luo, Haoyue Dai, Boyang Sun, Loka Li, Biwei Huang, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders. CoRR abs/2501.10124 (2025) - [i195]Minghao Fu, Biwei Huang, Zijian Li, Yujia Zheng, Ignavier Ng, Yingyao Hu, Kun Zhang:
Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System. CoRR abs/2501.12500 (2025) - [i194]Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang:
Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data. CoRR abs/2501.18990 (2025) - [i193]Yifan Shen, Peiyuan Zhu, Zijian Li, Shaoan Xie, Zeyu Tang, Namrata Deka, Zongfang Liu, Guangyi Chen, Kun Zhang:
Controllable Video Generation with Provable Disentanglement. CoRR abs/2502.02690 (2025) - [i192]Zeyu Tang, Zhenhao Chen, Loka Li, Xiangchen Song, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun Zhang:
Reflection-Window Decoding: Text Generation with Selective Refinement. CoRR abs/2502.03678 (2025) - [i191]Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, Kun Zhang:
Learning Counterfactual Outcomes Under Rank Preservation. CoRR abs/2502.06398 (2025) - [i190]Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han:
Noisy Test-Time Adaptation in Vision-Language Models. CoRR abs/2502.14604 (2025) - [i189]Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang:
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning. CoRR abs/2503.00639 (2025) - [i188]Junyi Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, Hui Xiong:
Can Large Language Models Help Experimental Design for Causal Discovery? CoRR abs/2503.01139 (2025) - [i187]Minghao Fu, Danning Li, Aryan Gadhiya, Benjamin Lambright, Mohamed Alowais, Mohab Bahnassy, Saad El Dine Elletter, Hawau Olamide Toyin, Haiyan Jiang, Kun Zhang, Hanan Aldarmaki:
Infant Cry Detection Using Causal Temporal Representation. CoRR abs/2503.06247 (2025) - [i186]Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang:
When Selection Meets Intervention: Additional Complexities in Causal Discovery. CoRR abs/2503.07302 (2025) - [i185]Jiaqi Sun, Yujia Zheng, Xinshuai Dong, Haoyue Dai, Kun Zhang:
Type Information-Assisted Self-Supervised Knowledge Graph Denoising. CoRR abs/2503.09916 (2025) - [i184]Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies. CoRR abs/2503.16544 (2025) - [i183]Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang:
Nonparametric Factor Analysis and Beyond. CoRR abs/2503.16865 (2025) - [i182]Zhengqing Gao, Dongting Hu, Jia-Wang Bian, Huan Fu, Yan Li, Tongliang Liu, Mingming Gong, Kun Zhang:
ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes. CoRR abs/2503.17486 (2025) - [i181]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Mingming Gong, Biwei Huang, Kun Zhang, Anton van den Hengel, Javen Qinfeng Shi:
Analytic DAG Constraints for Differentiable DAG Learning. CoRR abs/2503.19218 (2025) - [i180]Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge. CoRR abs/2504.13904 (2025) - [i179]Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang:
On the Thinking-Language Modeling Gap in Large Language Models. CoRR abs/2505.12896 (2025) - [i178]Dingling Yao, Shimeng Huang, Riccardo Cadei, Kun Zhang, Francesco Locatello:
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations. CoRR abs/2505.17708 (2025) - [i177]Hongzheng Yang, Yongqiang Chen, Zeyu Qin, Tongliang Liu, Chaowei Xiao, Kun Zhang, Bo Han:
Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment? CoRR abs/2505.18672 (2025) - [i176]Xiangchen Song, Aashiq Muhamed, Yujia Zheng, Lingjing Kong, Zeyu Tang, Mona T. Diab, Virginia Smith, Kun Zhang:
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs. CoRR abs/2505.20254 (2025) - [i175]Boyang Sun, Yu Yao, Xinshuai Dong, Zongfang Liu, Tongliang Liu, Yumou Qiu, Kun Zhang:
A Sample Efficient Conditional Independence Test in the Presence of Discretization. CoRR abs/2506.08747 (2025) - [i174]Tianjun Yao, Haoxuan Li, Zhiqiang Shen, Pan Li, Tongliang Liu, Kun Zhang:
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering. CoRR abs/2506.09645 (2025) - [i173]Weilin Chen, Wanyang Gu, Linjun Peng, Ruichu Cai, Zhifeng Hao, Kun Zhang:
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. CoRR abs/2507.06888 (2025) - [i172]Yan Li, Guangyi Chen, Yunlong Deng, Zijian Li, Zeyu Tang, Anpeng Wu, Kun Zhang:
Should Bias Always be Eliminated? A Principled Framework to Use Data Bias for OOD Generation. CoRR abs/2507.17001 (2025) - [i171]Shaoan Xie, Lingjing Kong, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang:
SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. CoRR abs/2507.22264 (2025) - 2024
- [i170]Xinshuai Dong, Haoyue Dai, Yewen Fan, Songyao Jin, Sathyamoorthy Rajendran, Kun Zhang:
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors. CoRR abs/2401.05414 (2024) - [i169]Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang:
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. CoRR abs/2401.09716 (2024) - [i168]Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang:
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process. CoRR abs/2401.14535 (2024) - [i167]Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang:
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models. CoRR abs/2401.16692 (2024) - [i166]Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang:
Natural Counterfactuals With Necessary Backtracking. CoRR abs/2402.01607 (2024) - [i165]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. CoRR abs/2402.03941 (2024) - [i164]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. CoRR abs/2402.05052 (2024) - [i163]Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models. CoRR abs/2402.06223 (2024) - [i162]Loka Li, Guangyi Chen, Yusheng Su, Zhenhao Chen, Yixuan Zhang, Eric P. Xing, Kun Zhang:
Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models. CoRR abs/2402.12563 (2024) - [i161]Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang, Guangyi Chen, Yifan Shen, Zhengming Chen, Xiangchen Song, Zhifeng Hao, Kun Zhang:
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting. CoRR abs/2402.12767 (2024) - [i160]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i159]Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang:
Counterfactual Generation with Identifiability Guarantees. CoRR abs/2402.15309 (2024) - [i158]Charvi Rastogi, Xiangchen Song, Zhijing Jin, Ivan Stelmakh, Hal Daumé III, Kun Zhang, Nihar B. Shah:
A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions. CoRR abs/2403.01015 (2024) - [i157]Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu:
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework. CoRR abs/2403.08743 (2024) - [i156]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. CoRR abs/2403.14843 (2024) - [i155]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. CoRR abs/2403.15500 (2024) - [i154]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Identifiable Latent Neural Causal Models. CoRR abs/2403.15711 (2024) - [i153]Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. CoRR abs/2404.13792 (2024) - [i152]Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Zhifeng Hao, Zhengmao Zhu, Guangyi Chen, Kun Zhang:
On the Identification of Temporally Causal Representation with Instantaneous Dependence. CoRR abs/2405.15325 (2024) - [i151]Ruichu Cai, Zhifang Jiang, Zijian Li, Weilin Chen, Xuexin Chen, Zhifeng Hao, Yifan Shen, Guangyi Chen, Kun Zhang:
From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. CoRR abs/2405.16083 (2024) - [i150]Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang:
Learning Discrete Concepts in Latent Hierarchical Models. CoRR abs/2406.00519 (2024) - [i149]Shunxing Fan, Mingming Gong, Kun Zhang:
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. CoRR abs/2406.02191 (2024) - [i148]Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter L. Spirtes, Yang Liu, Lu Cheng:
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. CoRR abs/2406.06736 (2024) - [i147]Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang:
Learning Discrete Latent Variable Structures with Tensor Rank Conditions. CoRR abs/2406.07020 (2024) - [i146]Tianjun Yao, Jiaqi Sun, Defu Cao, Kun Zhang, Guangyi Chen:
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification. CoRR abs/2406.19832 (2024) - [i145]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. CoRR abs/2407.00529 (2024) - [i144]Zhiqiang Xie, Yujia Zheng, Lizi Ottens, Kun Zhang, Christos Kozyrakis, Jonathan Mace:
Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight. CoRR abs/2407.08694 (2024) - [i143]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR abs/2407.10132 (2024) - [i142]Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang:
Empowering Graph Invariance Learning with Deep Spurious Infomax. CoRR abs/2407.11083 (2024) - [i141]Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
On the Parameter Identifiability of Partially Observed Linear Causal Models. CoRR abs/2407.16975 (2024) - [i140]Yuqin Yang, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami:
Causal Discovery in Linear Models with Unobserved Variables and Measurement Error. CoRR abs/2407.19426 (2024) - [i139]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Xiangwei Chen, Zexu Sun, Fei Wu, Kun Zhang:
Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression. CoRR abs/2408.05428 (2024) - [i138]Boyang Sun, Ignavier Ng, Guangyi Chen, Yifan Shen, Qirong Ho, Kun Zhang:
Continual Learning of Nonlinear Independent Representations. CoRR abs/2408.05788 (2024) - [i137]Ce Chen, Shaoli Huang, Xuelin Chen, Guangyi Chen, Xiaoguang Han, Kun Zhang, Mingming Gong:
CT4D: Consistent Text-to-4D Generation with Animatable Meshes. CoRR abs/2408.08342 (2024) - [i136]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. CoRR abs/2408.10353 (2024) - [i135]Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang:
Causal Temporal Representation Learning with Nonstationary Sparse Transition. CoRR abs/2409.03142 (2024) - [i134]Berker Demirel, Lingjing Kong, Kun Zhang, Theofanis Karaletsos, Celestine Mendler-Dünner, Francesco Locatello:
Adjusting Pretrained Backbones for Performativity. CoRR abs/2410.04499 (2024) - [i133]Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yi-An Ma, Biwei Huang:
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. CoRR abs/2410.06407 (2024) - [i132]Anpeng Wu, Kun Kuang, Minqin Zhu, Yingrong Wang, Yujia Zheng, Kairong Han, Baohong Li, Guangyi Chen, Fei Wu, Kun Zhang:
Causality for Large Language Models. CoRR abs/2410.15319 (2024) - [i131]Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang:
Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyond. CoRR abs/2410.18396 (2024) - [i130]Yiwen Qiu, Yujia Zheng, Kun Zhang:
Identifying Selections for Unsupervised Subtask Discovery. CoRR abs/2410.21616 (2024) - [i129]Klea Ziu, Slavomír Hanzely, Loka Li, Kun Zhang, Martin Takác, Dmitry Kamzolov:
ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. CoRR abs/2410.23862 (2024) - [i128]Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva, Kun Zhang, Eric P. Xing, Willie Neiswanger, Qirong Ho:
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness. CoRR abs/2411.03731 (2024) - [i127]Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang:
Causal Representation Learning from Multimodal Biological Observations. CoRR abs/2411.06518 (2024) - [i126]Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang:
Differentiable Causal Discovery For Latent Hierarchical Causal Models. CoRR abs/2411.19556 (2024) - 2023
- [i125]Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang:
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. CoRR abs/2301.08987 (2023) - [i124]Yijun Bian, Kun Zhang:
Increasing Fairness in Compromise on Accuracy via Weighted Vote with Learning Guarantees. CoRR abs/2301.10813 (2023) - [i123]Devansh Arpit, Matthew Fernandez, Chenghao Liu, Weiran Yao, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen C. H. Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles:
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data. CoRR abs/2301.10859 (2023) - [i122]Guanglin Zhou, Lina Yao, Xiwei Xu, Chen Wang, Liming Zhu, Kun Zhang:
On the Opportunity of Causal Deep Generative Models: A Survey and Future Directions. CoRR abs/2301.12351 (2023) - [i121]Zheng-Mao Zhu, Yu-Ren Liu, Hong-Long Tian, Yang Yu, Kun Zhang:
Beware of Instantaneous Dependence in Reinforcement Learning. CoRR abs/2303.05458 (2023) - [i120]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CoRR abs/2304.02146 (2023) - [i119]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CoRR abs/2304.03265 (2023) - [i118]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CoRR abs/2304.03382 (2023) - [i117]Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang:
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction. CoRR abs/2304.04298 (2023) - [i116]Hanqi Yan, Lin Gui, Menghan Wang, Kun Zhang, Yulan He:
Explainable Recommender with Geometric Information Bottleneck. CoRR abs/2305.05331 (2023) - [i115]Jiaqi Sun, Lin Zhang, Guangyi Chen, Kun Zhang, Peng Xu, Yujiu Yang:
Feature Expansion for Graph Neural Networks. CoRR abs/2305.06142 (2023) - [i114]Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang:
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. CoRR abs/2305.11379 (2023) - [i113]Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schölkopf, Zhijing Jin, Rada Mihalcea:
Voices of Her: Analyzing Gender Differences in the AI Publication World. CoRR abs/2305.14597 (2023) - [i112]Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov, Kun Zhang:
Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data. CoRR abs/2305.18410 (2023) - [i111]Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang:
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. CoRR abs/2305.19582 (2023) - [i110]Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang:
Understanding Masked Autoencoders via Hierarchical Latent Variable Models. CoRR abs/2306.04898 (2023) - [i109]Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang:
Advancing Counterfactual Inference through Quantile Regression. CoRR abs/2306.05751 (2023) - [i108]Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang:
Partial Identifiability for Domain Adaptation. CoRR abs/2306.06510 (2023) - [i107]Yu-Ren Liu, Biwei Huang, Zheng-Mao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang:
Learning World Models with Identifiable Factorization. CoRR abs/2306.06561 (2023) - [i106]Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. CoRR abs/2306.06931 (2023) - [i105]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. CoRR abs/2306.07916 (2023) - [i104]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. CoRR abs/2307.16405 (2023) - [i103]Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang:
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. CoRR abs/2308.06718 (2023) - [i102]Guangyi Chen, Xiao Liu, Guangrun Wang, Kun Zhang, Philip H. S. Torr, Xiao-Ping Zhang, Yansong Tang:
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer. CoRR abs/2308.08414 (2023) - [i101]Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman H. Khan, Kun Zhang, Fahad Shahbaz Khan:
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment. CoRR abs/2308.12960 (2023) - [i100]Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang:
Subspace Identification for Multi-Source Domain Adaptation. CoRR abs/2310.04723 (2023) - [i99]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Identifiable Latent Polynomial Causal Models Through the Lens of Change. CoRR abs/2310.15580 (2023) - [i98]Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang:
Temporally Disentangled Representation Learning under Unknown Nonstationarity. CoRR abs/2310.18615 (2023) - [i97]Yujia Zheng, Kun Zhang:
Generalizing Nonlinear ICA Beyond Structural Sparsity. CoRR abs/2311.00866 (2023) - [i96]Zijian Li, Zunhong Xu, Ruichu Cai, Zhenhui Yang, Yuguang Yan, Zhifeng Hao, Guangyi Chen, Kun Zhang:
Identifying Semantic Component for Robust Molecular Property Prediction. CoRR abs/2311.04837 (2023) - [i95]Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. CoRR abs/2311.14688 (2023) - [i94]Shaoan Xie, Yang Zhao, Zhisheng Xiao, Kelvin C. K. Chan, Yandong Li, Yanwu Xu, Kun Zhang, Tingbo Hou:
DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models. CoRR abs/2312.03771 (2023) - [i93]Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang:
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation. CoRR abs/2312.07424 (2023) - [i92]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. CoRR abs/2312.11001 (2023) - [i91]Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang:
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants. CoRR abs/2312.11934 (2023) - [i90]Yuke Li, Lixiong Chen, Guangyi Chen, Ching-Yao Chan, Kun Zhang, Stefano Anzellotti, Donglai Wei:
Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction. CoRR abs/2312.14373 (2023) - 2022
- [i89]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. CoRR abs/2201.05666 (2022) - [i88]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal transport for causal discovery. CoRR abs/2201.09366 (2022) - [i87]Weiran Yao, Guangyi Chen, Kun Zhang:
Learning Latent Causal Dynamics. CoRR abs/2202.04828 (2022) - [i86]Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning with Kernel. CoRR abs/2202.05458 (2022) - [i85]Zeyu Tang, Kun Zhang:
Attainability and Optimality: The Equalized Odds Fairness Revisited. CoRR abs/2202.11853 (2022) - [i84]Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich:
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. CoRR abs/2203.12707 (2022) - [i83]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. CoRR abs/2203.16582 (2022) - [i82]Yewen Fan, Nian Si, Kun Zhang:
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. CoRR abs/2205.09809 (2022) - [i81]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i80]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i79]Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian, Kun Zhang, Yang Yu:
Offline Reinforcement Learning with Causal Structured World Models. CoRR abs/2206.01474 (2022) - [i78]Zeyu Tang, Jiji Zhang, Kun Zhang:
What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective. CoRR abs/2206.04101 (2022) - [i77]Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang:
Causal Balancing for Domain Generalization. CoRR abs/2206.05263 (2022) - [i76]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. CoRR abs/2206.07751 (2022) - [i75]Wenzhuo Yang, Kun Zhang, Steven C. H. Hoi:
Causality-Based Multivariate Time Series Anomaly Detection. CoRR abs/2206.15033 (2022) - [i74]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Weight-variant Latent Causal Models. CoRR abs/2208.14153 (2022) - [i73]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Kun Zhang, Javen Qinfeng Shi:
Identifying Latent Causal Content for Multi-Source Domain Adaptation. CoRR abs/2208.14161 (2022) - [i72]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, M. Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi:
Truncated Matrix Power Iteration for Differentiable DAG Learning. CoRR abs/2208.14571 (2022) - [i71]Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang:
Prompt Learning with Optimal Transport for Vision-Language Models. CoRR abs/2210.01253 (2022) - [i70]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR abs/2210.01798 (2022) - [i69]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. CoRR abs/2210.05955 (2022) - [i68]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. CoRR abs/2210.11021 (2022) - [i67]Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. CoRR abs/2210.13647 (2022) - [i66]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks. CoRR abs/2211.00261 (2022) - [i65]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. CoRR abs/2211.03984 (2022) - [i64]Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang:
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model. CoRR abs/2212.05034 (2022) - 2021
- [i63]Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour:
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR abs/2103.14238 (2021) - [i62]Yao-Hung Hubert Tsai, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations. CoRR abs/2106.02866 (2021) - [i61]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i60]Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang:
Graph Domain Adaptation: A Generative View. CoRR abs/2106.07482 (2021) - [i59]Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang:
Progressive Open-Domain Response Generation with Multiple Controllable Attributes. CoRR abs/2106.14614 (2021) - [i58]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. CoRR abs/2107.02729 (2021) - [i57]Yige Zhang, Weixiong Rao, Kun Zhang, Lei Chen:
Outdoor Position Recovery from HeterogeneousTelco Cellular Data. CoRR abs/2108.10613 (2021) - [i56]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR abs/2109.02986 (2021) - [i55]Kai Zhang, Chao Tian, Kun Zhang, Todd Johnson, Xiaoqian Jiang:
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy. CoRR abs/2109.04626 (2021) - [i54]Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang:
Unaligned Image-to-Image Translation by Learning to Reweight. CoRR abs/2109.11736 (2021) - [i53]Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang:
Learning Temporally Causal Latent Processes from General Temporal Data. CoRR abs/2110.05428 (2021) - [i52]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR abs/2110.05721 (2021) - [i51]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. CoRR abs/2110.09356 (2021) - [i50]Zijian Li, Ruichu Cai, Tom Z. J. Fu, Kun Zhang:
Transferable Time-Series Forecasting under Causal Conditional Shift. CoRR abs/2111.03422 (2021) - 2020
- [i49]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour:
Domain Adaptation As a Problem of Inference on Graphical Models. CoRR abs/2002.03278 (2020) - [i48]Naji Shajarisales, Peter Spirtes, Kun Zhang:
Learning from Positive and Unlabeled Data by Identifying the Annotation Process. CoRR abs/2003.01067 (2020) - [i47]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. CoRR abs/2005.01095 (2020) - [i46]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. CoRR abs/2006.10201 (2020) - [i45]Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi:
Adaptive Task Sampling for Meta-Learning. CoRR abs/2007.08735 (2020) - [i44]Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang:
Attack on Multi-Node Attention for Object Detection. CoRR abs/2008.06822 (2020) - [i43]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. CoRR abs/2010.04917 (2020) - [i42]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How Do Fair Decisions Fare in Long-term Qualification? CoRR abs/2010.11300 (2020) - [i41]Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf:
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR abs/2012.09092 (2020) - [i40]Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao:
Learning Disentangled Semantic Representation for Domain Adaptation. CoRR abs/2012.11807 (2020) - 2019
- [i39]Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representation for Domain Adaptation. CoRR abs/1901.09453 (2019) - [i38]Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour:
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR abs/1902.10073 (2019) - [i37]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i36]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. CoRR abs/1904.01612 (2019) - [i35]Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA. CoRR abs/1904.09096 (2019) - [i34]Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery with Cascade Nonlinear Additive Noise Models. CoRR abs/1905.09442 (2019) - [i33]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. CoRR abs/1905.10857 (2019) - [i32]Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. CoRR abs/1906.01732 (2019) - [i31]Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxiliary Classifiers GAN. CoRR abs/1907.02690 (2019) - [i30]Yipeng Mou, Mingming Gong, Huan Fu, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao:
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach. CoRR abs/1907.06882 (2019) - [i29]Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan:
Domain Generalization via Multidomain Discriminant Analysis. CoRR abs/1907.11216 (2019) - [i28]Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour:
Identification of Effective Connectivity Subregions. CoRR abs/1908.03264 (2019) - [i27]Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang:
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. CoRR abs/1908.03932 (2019) - [i26]Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao:
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery. CoRR abs/1909.01525 (2019) - [i25]M. Reza Heydari, Saber Salehkaleybar, Kun Zhang:
Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference. CoRR abs/1909.04454 (2019) - [i24]AmirEmad Ghassami, Kun Zhang, Negar Kiyavash:
Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs. CoRR abs/1910.12993 (2019) - [i23]Jie Qiao, Zijian Li, Boyan Xu, Ruichu Cai, Kun Zhang:
Disentanglement Challenge: From Regularization to Reconstruction. CoRR abs/1912.00155 (2019) - [i22]Yige Zhang, Aaron Yi Ding, Jörg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao:
Transfer Learning-Based Outdoor Position Recovery with Telco Data. CoRR abs/1912.04521 (2019) - [i21]Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si:
Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs. CoRR abs/1912.05977 (2019) - 2018
- [i20]Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich:
Causal Generative Domain Adaptation Networks. CoRR abs/1804.04333 (2018) - [i19]Menghan Wang, Xiaolin Zheng, Kun Zhang:
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning. CoRR abs/1804.10795 (2018) - [i18]Ruibo Tu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström, Kun Zhang:
Causal discovery in the presence of missing data. CoRR abs/1807.04010 (2018) - [i17]Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao:
Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping. CoRR abs/1809.05852 (2018) - 2017
- [i16]Jalal Etesami, Kun Zhang, Negar Kiyavash:
A New Measure of Conditional Dependence for Causal Structural Learning. CoRR abs/1704.00607 (2017) - [i15]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. CoRR abs/1705.09644 (2017) - [i14]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR abs/1706.03768 (2017) - [i13]Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström:
Causality Refined Diagnostic Prediction. CoRR abs/1711.10915 (2017) - [i12]Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang:
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. CoRR abs/1711.11458 (2017) - 2016
- [i11]Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal:
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. CoRR abs/1603.04319 (2016) - 2015
- [i10]Kun Zhang, Jiji Zhang, Bernhard Schölkopf:
Distinguishing Cause from Effect Based on Exogeneity. CoRR abs/1504.05651 (2015) - [i9]Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu:
Towards Robust and Specific Causal Discovery from fMRI. CoRR abs/1509.08056 (2015) - 2013
- [i8]Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen:
Bridging Information Criteria and Parameter Shrinkage for Model Selection. CoRR abs/1307.2307 (2013) - 2012
- [i7]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. CoRR abs/1202.3775 (2012) - [i6]Jakob Zscheischler, Dominik Janzing, Kun Zhang:
Testing whether linear equations are causal: A free probability theory approach. CoRR abs/1202.3779 (2012) - [i5]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. CoRR abs/1203.3475 (2012) - [i4]Kun Zhang, Aapo Hyvärinen:
Source Separation and Higher-Order Causal Analysis of MEG and EEG. CoRR abs/1203.3533 (2012) - [i3]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. CoRR abs/1203.3534 (2012) - [i2]Kun Zhang, Aapo Hyvärinen:
On the Identifiability of the Post-Nonlinear Causal Model. CoRR abs/1205.2599 (2012) - 2011
- [i1]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang:
Robust Learning via Cause-Effect Models. CoRR abs/1112.2738 (2011)
Coauthor Index
Lai-Wan Chan
aka: Laiwan Chan
aka: Laiwan Chan
[c202] [c200] [c199] [c195] [c190] [c184] [c183] [c178] [c177] [c176] [c173] [c172] [c167] [c157] [c156] [c155] [c152] [c149] [c146] [c144] [c142] [c141] [c139] [c130] [c126] [c122] [c104] [c95] [i197] [i193] [i192] [i189] [i172] [i171] [i168] [i162] [i161] [i160] [i152] [i151] [i150] [i146] [i138] [i137] [i135] [i132] [i127] [i117] [i115] [i110] [i108] [i102] [i101] [i100] [i98] [i96] [i90] [i87] [i71] [i67]
[j46] [j45] [j40] [j39] [j21] [c199] [c198] [c193] [c187] [c170] [c166] [c164] [c160] [c154] [c145] [c136] [c129] [c124] [c112] [c111] [c106] [c102] [c99] [c93] [c89] [c87] [c84] [c83] [c82] [c81] [c78] [c77] [c75] [c68] [c67] [c65] [c64] [c59] [c58] [c55] [c50] [c47] [c44] [c43] [c37] [c36] [c33] [c32] [c31] [i182] [i181] [i165] [i163] [i154] [i152] [i149] [i143] [i137] [i133] [i107] [i104] [i99] [i84] [i81] [i80] [i74] [i73] [i72] [i69] [i61] [i56] [i54] [i49] [i36] [i33] [i31] [i30] [i26] [i20] [i17] [i14]
[j45] [j43] [j40] [j38] [j16] [c198] [c193] [c191] [c182] [c179] [c174] [c173] [c172] [c170] [c165] [c164] [c159] [c158] [c157] [c155] [c151] [c125] [c124] [c110] [c105] [c103] [c100] [c98] [c92] [c81] [c75] [c74] [c64] [c55] [c48] [c45] [c40] [c39] [c34] [c30] [i196] [i195] [i181] [i169] [i166] [i163] [i160] [i154] [i150] [i143] [i141] [i133] [i131] [i126] [i120] [i109] [i107] [i105] [i104] [i103] [i99] [i92] [i83] [i74] [i73] [i70] [i63] [i58] [i52] [i49] [i43] [i41] [i38] [i37] [i33] [i20] [i9]
Roberto Legaspi
aka: Roberto Sebastian Legaspi
aka: Roberto Sebastian Legaspi
[c203] [c200] [c198] [c196] [c193] [c191] [c180] [c179] [c175] [c174] [c172] [c168] [c165] [c159] [c135] [c123] [c116] [c115] [c102] [c99] [c94] [c86] [c72] [i195] [i194] [i189] [i186] [i181] [i164] [i160] [i156] [i155] [i141] [i138] [i136] [i133] [i131] [i126] [i120] [i114] [i92] [i89] [i81] [i76] [i72] [i51] [i46]
Qinfeng Shi
aka: Javen Qinfeng Shi
aka: Javen Qinfeng Shi
Peter Spirtes
aka: Peter L. Spirtes
aka: Peter L. Spirtes
[j40] [c205] [c204] [c202] [c200] [c195] [c190] [c180] [c174] [c168] [c162] [c153] [c152] [c135] [c128] [c123] [c120] [c114] [c104] [c94] [c86] [i195] [i189] [i185] [i183] [i176] [i171] [i164] [i156] [i145] [i144] [i136] [i135] [i132] [i130] [i127] [i114] [i112] [i108] [i104] [i97] [i92] [i89] [i76] [i66]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-08-22 07:01 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
