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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://dblp.dagstuhl.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
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2020 – today
- 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) - [c170]Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman H. Khan, Kun Zhang, Fahad Shahbaz Khan:
S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment. AAAI 2024: 7278-7286 - [c169]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 - [c168]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 - [c167]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. AISTATS 2024: 154-162 - [c166]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR 2024: 71-105 - [c165]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 - [c164]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 - [c163]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 - [c162]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 - [c161]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 - [c160]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c159]Xiu-Chuan Li, Kun Zhang, Tongliang Liu:
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. ICLR 2024 - [c158]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 - [c157]Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. ICLR 2024 - [c156]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. ICML 2024 - [c155]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 - [c154]Shunxing Fan, Mingming Gong, Kun Zhang:
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. ICML 2024 - [c153]Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang:
Score-Based Causal Discovery of Latent Variable Causal Models. ICML 2024 - [c152]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 - [c151]Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang:
Empowering Graph Invariance Learning with Deep Spurious Infomax. ICML 2024 - [c150]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. ICML 2024 - [c149]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 - [c148]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 - [i165]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) - [i164]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) - [i163]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) - [i162]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) - [i161]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) - [i160]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. CoRR abs/2402.05052 (2024) - [i159]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) - [i158]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) - [i157]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) - [i156]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) - [i155]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) - [i154]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) - [i153]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) - [i152]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. CoRR abs/2403.14843 (2024) - [i151]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) - [i150]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) - [i149]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) - [i148]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) - [i147]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) - [i146]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) - [i145]Shunxing Fan, Mingming Gong, Kun Zhang:
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. CoRR abs/2406.02191 (2024) - [i144]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) - [i143]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) - [i142]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) - [i141]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. CoRR abs/2407.00529 (2024) - [i140]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) - [i139]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) - [i138]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) - [i137]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) - [i136]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) - [i135]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) - [i134]Boyang Sun, Ignavier Ng, Guangyi Chen, Yifan Shen, Qirong Ho, Kun Zhang:
Continual Learning of Nonlinear Independent Representations. CoRR abs/2408.05788 (2024) - [i133]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) - [i132]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. CoRR abs/2408.10353 (2024) - [i131]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) - [i130]Berker Demirel, Lingjing Kong, Kun Zhang, Theofanis Karaletsos, Celestine Mendler-Dünner, Francesco Locatello:
Adjusting Pretrained Backbones for Performativity. CoRR abs/2410.04499 (2024) - [i129]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) - [i128]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) - [i127]Yiwen Qiu, Yujia Zheng, Kun Zhang:
Identifying Selections for Unsupervised Subtask Discovery. CoRR abs/2410.21616 (2024) - [i126]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) - 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) - [c147]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 - [c146]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CLeaR 2023: 752-771 - [c145]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 - [c144]Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang:
Unpaired Image-to-Image Translation with Shortest Path Regularization. CVPR 2023: 10177-10187 - [c143]Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang:
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction. CVPR 2023: 17874-17884 - [c142]Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang:
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model. CVPR 2023: 22428-22437 - [c141]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 - [c140]Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang:
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. ICLR 2023 - [c139]Yewen Fan, Nian Si, Kun Zhang:
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. ICLR 2023 - [c138]Junlong Li, Guangyi Chen, Yansong Tang, Jinan Bao, Kun Zhang, Jie Zhou, Jiwen Lu:
GAIN: On the Generalization of Instructional Action Understanding. ICLR 2023 - [c137]Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang:
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. ICLR 2023 - [c136]Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang:
Causal Balancing for Domain Generalization. ICLR 2023 - [c135]Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang:
Multi-domain image generation and translation with identifiability guarantees. ICLR 2023 - [c134]Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang:
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. ICLR 2023 - [c133]Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang:
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. ICML 2023: 3380-3407 - [c132]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 - [c131]Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu:
Model Transferability with Responsive Decision Subjects. ICML 2023: 4921-4952 - [c130]Yang Liu, Hao Cheng, Kun Zhang:
Identifiability of Label Noise Transition Matrix. ICML 2023: 21475-21496 - [c129]Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang:
Feature Expansion for Graph Neural Networks. ICML 2023: 33156-33176 - [c128]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 - [c127]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 - [c126]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 - [c125]Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang:
Subspace Identification for Multi-Source Domain Adaptation. NeurIPS 2023 - [c124]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. NeurIPS 2023 - [c123]Yuren Liu, Biwei Huang, Zhengmao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang:
Learning World Models with Identifiable Factorization. NeurIPS 2023 - [c122]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. NeurIPS 2023 - [c121]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 - [c120]Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang:
Counterfactual Generation with Identifiability Guarantees. NeurIPS 2023 - [c119]Yujia Zheng, Kun Zhang:
Generalizing Nonlinear ICA Beyond Structural Sparsity. NeurIPS 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
- [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) - [c118]Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan:
Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. AAAI 2022: 5942-5949 - [c117]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 - [c116]Zheng-Mao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang:
Invariant Action Effect Model for Reinforcement Learning. AAAI 2022: 9260-9268 - [c115]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. AISTATS 2022: 8095-8111 - [c114]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 - [c113]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 - [c112]Zeyu Tang, Kun Zhang:
Attainability and Optimality: The Equalized Odds Fairness Revisited. CLeaR 2022: 754-786 - [c111]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 - [c110]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 - [c109]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR 2022 - [c108]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 - [c107]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal Transport for Causal Discovery. ICLR 2022 - [c106]Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang:
Learning Temporally Causal Latent Processes from General Temporal Data. ICLR 2022 - [c105]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 - [c104]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 - [c103]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 - [c102]Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022: 24370-24387 - [c101]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 - [c100]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. NeurIPS 2022 - [c99]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS 2022 - [c98]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 - [c97]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS 2022 - [c96]Shaoan Xie, Qirong Ho, Kun Zhang:
Unsupervised Image-to-Image Translation with Density Changing Regularization. NeurIPS 2022 - [c95]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 - [c94]Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. NeurIPS 2022 - [c93]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. NeurIPS 2022 - [c92]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 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] - [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
- [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) - [c91]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 - [c90]Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang:
Testing Independence Between Linear Combinations for Causal Discovery. AAAI 2021: 6538-6546 - [c89]Ni Y. Lu, Kun Zhang, Changhe Yuan:
Improving Causal Discovery By Optimal Bayesian Network Learning. AAAI 2021: 8741-8748 - [c88]Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang:
Unaligned Image-to-Image Translation by Learning to Reweight. ICCV 2021: 14154-14164 - [c87]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 - [c86]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 - [c85]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NeurIPS 2021: 20308-20320 - [c84]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 - [c83]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 - [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
- [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) - [c82]Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du:
Compressed Self-Attention for Deep Metric Learning. AAAI 2020: 3561-3568 - [c81]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. AAAI 2020: 6526-6533 - [c80]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020: 10153-10161 - [c79]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 - [c78]AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang:
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML 2020: 3494-3504 - [c77]Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao:
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020: 3843-3853 - [c76]Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao:
Label-Noise Robust Domain Adaptation. ICML 2020: 10913-10924 - [c75]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 - [c74]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS 2020 - [c73]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS 2020 - [c72]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. NeurIPS 2020 - [c71]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NeurIPS 2020 - [c70]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 - [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] - [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)
2010 – 2019
- 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) - [c69]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019: 3664-3671 - [c68]Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang:
Causal Discovery in the Presence of Missing Data. AISTATS 2019: 1762-1770 - [c67]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019: 3449-3458 - [c66]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019: 3487-3496 - [c65]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 - [c64]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 - [c63]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019: 2901-2910 - [c62]Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representations for Domain Adaptation. ICML 2019: 7523-7532 - [c61]Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019: 1609-1615 - [c60]Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao:
Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019: 2060-2066 - [c59]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 - [c58]Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxilary Classifiers GAN. NeurIPS 2019: 1328-1337 - [c57]Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao:
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. NeurIPS 2019: 6880-6890 - [c56]Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NeurIPS 2019: 12773-12784 - [c55]Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang:
Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019: 12863-12872 - [c54]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 - [c53]Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019: 186-195 - [c52]Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan:
Domain Generalization via Multidomain Discriminant Analysis. UAI 2019: 292-302 - [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] - [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
- [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) - [c51]Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang:
Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. AAAI 2018: 2516-2523 - [c50]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang:
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018: 4000-4007 - [c49]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 - [c48]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 - [c47]Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour:
Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560 - [c46]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 - [c45]Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery from Discrete Data using Hidden Compact Representation. NeurIPS 2018: 2671-2679 - [c44]AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang:
Multi-domain Causal Structure Learning in Linear Systems. NeurIPS 2018: 6269-6279 - [c43]Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang:
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. NeurIPS 2018: 6670-6679 - [c42]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 - [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] - [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
- [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) - [c41]Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan:
Causal Discovery Using Regression-Based Conditional Independence Tests. AAAI 2017: 1250-1256 - [c40]Kun Zhang:
Causal Learning and Machine Learning. AMBN 2017: 4 - [c39]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 - [c38]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 - [c37]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. NIPS 2017: 3011-3021 - [c36]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao:
Causal Discovery from Temporally Aggregated Time Series. UAI 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
- [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) - [c35]Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. ICML 2016: 2839-2848 - [c34]Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal:
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. UAI 2016 - [c33]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 - [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
- [c32]Kun Zhang, Mingming Gong, Bernhard Schölkopf:
Multi-Source Domain Adaptation: A Causal View. AAAI 2015: 3150-3157 - [c31]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Dacheng Tao, Philipp Geiger:
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015: 1898-1906 - [c30]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 - [c29]Biwei Huang, Kun Zhang, Bernhard Schölkopf:
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015: 3561-3568 - [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) - 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) - [c28]Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf:
A Permutation-Based Kernel Conditional Independence Test. UAI 2014: 132-141 - 2013
- [c27]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 - [c26]Kun Zhang, Zhikun Wang, Bernhard Schölkopf:
On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example. ICDM Workshops 2013: 139-146 - [c25]Zhitang Chen, Kun Zhang, Laiwan Chan:
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. ICDM 2013: 1003-1008 - [c24]Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang:
Domain Adaptation under Target and Conditional Shift. ICML (3) 2013: 819-827 - [i8]Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen:
Bridging Information Criteria and Parameter Shrinkage for Model Selection. CoRR abs/1307.2307 (2013) - 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) - [c23]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. ICML 2012 - [c22]Zhitang Chen, Kun Zhang, Laiwan Chan:
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. NIPS 2012: 1736-1744 - [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
- [c21]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011: 804-813 - [c20]Jakob Zscheischler, Dominik Janzing, Kun Zhang:
Testing whether linear equations are causal: A free probability theory approach. UAI 2011: 839-846 - [c19]Kun Zhang, Aapo Hyvärinen:
A General Linear Non-Gaussian State-Space Model. ACML 2011: 113-128 - [i1]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang:
Robust Learning via Cause-Effect Models. CoRR abs/1112.2738 (2011) - 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) - [c18]Min-Ling Zhang, Kun Zhang:
Multi-label learning by exploiting label dependency. KDD 2010: 999-1008 - [c17]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 - [c16]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. UAI 2010: 143-150 - [c15]Kun Zhang, Aapo Hyvärinen:
Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI 2010: 709-716 - [c14]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010: 717-724 - [c13]Kun Zhang, Aapo Hyvärinen:
Nonlinear acyclic causal models. NIPS Causality: Objectives and Assessment 2010: 157-164
2000 – 2009
- 2009
- [c12]Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen:
ICA with Sparse Connections: Revisited. ICA 2009: 195-202 - [c11]Kun Zhang, Aapo Hyvärinen:
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD (2) 2009: 570-585 - [c10]Kun Zhang, Aapo Hyvärinen:
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009: 647-655 - 2007
- [j5]Kun Zhang, Laiwan Chan:
Separating Convolutive Mixtures By Pairwise Mutual Information Minimization. IEEE Signal Process. Lett. 14(12): 992-995 (2007) - [c9]Kun Zhang, Laiwan Chan:
Kernel-Based Nonlinear Independent Component Analysis. ICA 2007: 301-308 - [c8]Kun Zhang, Laiwan Chan:
Nonlinear independent component analysis with minimal nonlinear distortion. ICML 2007: 1127-1134 - [c7]Jian Li, Kun Zhang, Laiwan Chan:
Independent Factor Reinforcement Learning for Portfolio Management. IDEAL 2007: 1020-1031 - 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) - [c6]Kun Zhang, Lai-Wan Chan:
ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments. ICA 2006: 311-318 - [c5]Kun Zhang, Lai-Wan Chan:
Enhancement of Source Independence for Blind Source Separation. ICA 2006: 731-738 - [c4]Kun Zhang, Lai-Wan Chan:
Extensions of ICA for Causality Discovery in the Hong Kong Stock Market. ICONIP (3) 2006: 400-409 - [c3]Kun Zhang, Lai-Wan Chan:
ICA with Sparse Connections. IDEAL 2006: 530-537 - 2005
- [b1]Kun Zhang:
Extensions of independent component analysis: towards applications. Chinese University of Hong Kong, Hong Kong, 2005 - [j1]Kun Zhang, Lai-Wan Chan:
Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures. Neural Comput. 17(2): 425-452 (2005) - [c2]Kun Zhang, Lai-Wan Chan:
To apply score function difference based ICA algorithms to high-dimensional data. ESANN 2005: 291-296 - 2003
- [c1]Kun Zhang, Lai-Wan Chan:
Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA. ICANN 2003: 132-139
Coauthor Index
Lai-Wan Chan
aka: Laiwan Chan
aka: Laiwan Chan
[j40] [j39] [c158] [c154] [c152] [i161] [i159] [i150] [i148] [i145] [i139] [i133] [i129] [c144] [c135] [c128] [c123] [i107] [i104] [i99] [c111] [c110] [c105] [c101] [c98] [c92] [i84] [i81] [i80] [i74] [i73] [i72] [i69] [j21] [c88] [c86] [c83] [i61] [i56] [i54] [c82] [c81] [c80] [c77] [c76] [c74] [i49] [c67] [c66] [c64] [c63] [c58] [c57] [c54] [i36] [i33] [i31] [i30] [i26] [c49] [c46] [c43] [c42] [i20] [i17] [c36] [i14] [c35] [c32] [c31] [c30]
[j40] [j38] [c169] [c166] [c162] [c161] [c160] [c158] [c153] [c152] [i164] [i159] [i156] [i150] [i146] [i139] [i137] [i129] [c124] [c123] [i120] [i109] [i107] [i105] [i104] [i103] [i99] [i92] [c109] [c104] [c102] [c99] [c97] [i83] [i74] [i73] [i70] [c91] [i63] [i58] [i52] [j16] [c80] [c74] [c73] [i49] [i43] [i41] [c63] [c54] [i38] [i37] [i33] [c47] [c44] [i20] [c39] [c38] [c33] [c29] [i9]
Peter Spirtes
aka: Peter L. Spirtes
aka: Peter L. Spirtes
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