Publications

  • Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric Xing, Clark Glymour, “Specific and Shared Causal Relation Modeling and Mechanism-based Clustering,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019

  • Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang, “Triad Constraints for Learning Causal Structure of Latent Variables,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019

  • Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich, “Twin Auxilary Classifiers GAN,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019

  • Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao, “Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019

  • Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellstrom, Cheng Zhang, “Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019

  • Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng, “PRNet: Outdoor Position Recovery for Heterogenous TelcoData by Deep Neural Network," accepted to the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing, China, November 2019

  • Clark Glymour, Kun Zhang, Peter Spirtes, “A Review of Causal Discovery Algorithms Based on Graphical Models,” Frontiers in Genetics, 2019 (link)

  • Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí, Egbert H. van Nes, Jonas Peters, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang, Jakob Zscheischler, “Inferring Causation from Time Series in Earth System Sciences and Beyond,” Nature Communications, 2019 (link)

  • Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Madelyn R. K. Glymour, Biwei Huang and Clark Glymour, “Estimating Feedforward and Feedback Effective Connections from FMRI Time Series: Assessments of Statistical Methods,” Network Neuroscience, 3(2): 274-306, 2019 (link)

  • Joseph Ramsey, Kun Zhang, Clark Glymour, “The Evaluation of Discovery: Models, Simulation and Search through ‘Big Data’,” Open Philosophy, 2 (1):39-48 2019 (link)

  • Eric Strobl, Kun Zhang, and S. Visweswaran, “Approximate kernel-based conditional independence tests for fast non-parametric causal discovery,” Journal of Causal Inference, 7 (1), 2019 (link)

  • Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen, “Causal Discovery with General Non-Linear Relationships Using Non-Linear Independent Component Analysis,” in Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2019 (link)

  • Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan, “Domain Generalization via Multi-domain Discriminant Analysis,” in Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2019

  • Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour, “Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models,” in Proceedings of International Conference on Machine Learning (ICML) 2019 (link)

  • Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon, “On Learning Invariant Representation for Domain Adaptation,” in Proceedings of International Conference on Machine Learning (ICML) 2019 (link)

  • Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao, “Causal Discovery with Cascade Nonlinear Additive Noise Model,” in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2019

  • Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao, “Learning Disentangled Semantic Representation for Domain Adaptation,” in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2019

  • Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao, “Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2019 Best Paper Finalist (link)

  • Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Clark Glymour, Hedvig Kjellström, Kun Zhang, “Causal discovery in the presence of missing data,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)

  • Petar Stojanov, Mingming Gong, Jaime Carbonell , Kun Zhang, “Low-Dimensional Density Ratio Estimation for Covariate Shift Correction,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)

  • Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang, “Data-Driven Approach to Multiple-Source Domain Adaptation,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)

  • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang, “Counting and Sampling from Markov Equivalent DAGs Using Clique Trees,” In Proceedings of AAAI 2019 (link)

  • Kun Zhang, Bernhard Schölkopf, Peter Spirtes, Clark Glymour, “Learning Causality and Causality-Related Learning,” National Science Review, January 2018 (link)

  • Kun Zhang and Madelyn Glymour, "Unmixing for Causal Inference: Thoughts on McCaffrey and Danks," The British Journal for the Philosophy of Science, 2018 (link)

  • Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang, “Modeling Dynamic Missingness of Implicit Feedback for Recommendation,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)

  • AmirEmad Ghassami, Biwei Huang, Negar Kiyavash, Kun Zhang, “Multi-Domain Causal Structure Learning in Linear Systems,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)

  • Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao, “Causal Discovery for Discrete Variables with Hidden Compact Representations,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)

  • Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao, “Deep Domain Generalization via Conditional Invariant Adversarial Networks,” In Proceedings of European Conference on Computer Vision (ECCV) 2018 (link)

  • Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour, “Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results,” in Proc. Conference on Uncertainty in Artificial Intelligence (UAI’18), CA, USA, August, 2018 (plenary session) (link)

  • Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, and Clark Glymur, “Generalized Score Functions for Causal Discovery,” in Proc. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD2018), London, August 2018 (long presentation) (link)

  • Saber SalehKaleyber, Jalal Etesami, Negar Kiyavash, and Kun Zhang, “Learning Vector Autoregressive Models with Latent Processes,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018 (link)

  • Menghan Wang, Xiaolin Zheng, Yang Yang, and Kun Zhang. Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018 (link)

  • Peter Spirtes and Kun Zhang, “Search for Causal Models,” In Handbook of Graphical Models, Chapman & Hall/CRC Handbooks of Modern Statistical Methods series, (Ed) M. Drton, S. Lauritzen, M. Wainwright, and M. Maathuis, 2018 (link)

  • Heng Peng, Tao Huang, and Kun Zhang, “Model Selection for Gaussian Mixture Models," Statistica Sinica, 2017 (link)

  • Aditya Menon, Chetali Gupta, Kedar M. Perkins, Brian L. DeCost, Nikita Budwal, Renee T. Rios, Kun Zhang, Barnabás Póczosd and Newell R. Washburn, “Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach,” Molecular Systems Design & Engineering, 2017 (link)

  • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang, “Learning Causal Structures Using Regression Invariance,” accepted to Advances in Neural Information Processing Systems 31 (NIPS 2017), 2017 (link)

  • Biwei Huang, Kun, Zhang, Jiji Zhang, Ruben Sanchez Romero, Clark Glymour, Bernhard Schölkopf, “Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows,” in Proceedings of IEEE 17th International Conference on Data Mining (ICDM 2017), 2017 (link)

  • Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, and Dacheng Tao, “Causal Discovery from Temporally Aggregated Time Series,” in Proc. Conference on Uncertainty in Artificial Intelligence (UAI’17), Sydney, Australia, August 11-15, 2017 (link)

  • Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour, “Causal Discovery in the Presence of Measurement Error: Identifiability Conditions,” UAI 2017 Workshop on Causality: Learning, Inference, and Decision-Making, Sydney, Australia, August 15, 2017 (link)

  • M. Klasson, Kun Zhang, B. Bertilson, C. Zhang and H. Kjellström, Causality Refined Diagnostic Prediction, NIPS Workshop on Machine Learning for Health (ML4H), 2017

  • Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf, "Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination," in Proc. International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, August, 2017 (link)

  • Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan, “Causal Discovery Using Regression-Based Conditional Independence Tests,” in Proc. 31th AAAI Conference on Artificial Intelligence (AAAI 2017), San Franceso, USA, Feb., 2017

  • Peter Spirtes and Kun Zhang, “Causal discovery and inference: Concepts and recent methodological advances,” Applied Informatics, 3(3), 2016 (link)

  • Kun Zhang, Zhikun Wang, Jiji Zhang, and Bernhard Schölkopf, “On estimation of functional causal models: Post-nonlinear causal model as an example,” ACM Transactions on Intelligent Systems and Technologies, 7(2), 2016 (link)

  • 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,” Proceedings of the 32rd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 (plenary talk, acceptance rate ~ 9%) (link)

  • Jalal Etesam, Negar Kiyavash, Kun Zhang, and Kushagra Singhal, “Learning Causal Interaction Network of Multivariate Hawkes Processes,” Proceedings of the 32rd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 (link)

  • Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf, “Domain Adaptation with Conditional Transferable Components,” Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 2016 (link)

  • Kun Zhang and Aapo Hyvärinen, “Nonlinear functional causal models for distinguishing cause from effect,” in Statistics and Causality: Methods for Applied Empirical Research, (Ed) W Wiedemann and A von Eye, August, 2016

  • Jiji Zhang and Kun Zhang, “Likelihood and Consilience: On Forster's Counterexamples to the Likelihood Theory of Evidence,” Philosophy of Science, Supplementary Volume 2015

  • Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, and Dominik Janzing, “Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components,” In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), (Ed) F. Bach and D. Blei, 37, JMLR W&CP, 1917–1925, Lille, France, July 2015 (link)

  • Mingming Gong*, Kun Zhang*, Bernhard Schölkopf, Dacheng Tao, and Philipp Geiger, “Discovering Temporal Causal Relations from Subsampled Data,” In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), (Ed) F. Bach and D. Blei, 37, JMLR W&CP, 1898–1906, Lille, France, July 2015 (link)

  • Kun Zhang, Jiji Zhang and Bernhard Schölkopf, "Distinguishing Cause from Effect Based on Exogeneity," In Proc. Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, (TARK 2015), Pittsburgh, PA, June 2015 (link)

  • Peter Spirtes and Kun Zhang. Recent Methodological Advances in Causal Discovery and Inference In Proc. 15th Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2015), Invited paper, Pittsburgh, PA, June 2015

  • Biwei Huang, Kun Zhang and Bernhard Schölkopf, “Time-dependent causal modeling: A Gaussian process treatment,” In Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI’15), Machine Learning Track, Argentina, July 2015 (link)

  • Kun Zhang, Mingming Gong and Bernhard Schölkopf, “Domain adaptation with multiple sources: A Causal view,” in Proc. 29th AAAI Conference on Artificial Intelligence (AAAI 2015), Austin Texas, USA, Jan., 2015 (link)

  • Zhitang Chen, Kun Zhang, Laiwan Chan, and Bernhard Schölkopf, “Causal discovery via reproducing kernel Hilbert space embeddings,” Neural Computation, 26(7):1484-517, 2014

  • Gary Doran, Krikamol Muandet, Kun Zhang, and Bernhard Schölkopf, “A permutation-based kernel conditional independence test,” in Proc. 30th Uncertainty in Artificial Intelligence (UAI 2014), Canada, July 2014 (plenary talk, acceptance rate ~ 10%)

  • Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, Zhi-Hua Zhou, and Claudio Persello, “Single-source domain adaptation with target and conditional shift,” in Regularization, Optimization, Kernels, and Support Vector Machines, J. A. K. Suykens, M. Signoretto, and Andreas Argyriou (Editors), 2014

  • Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang, "Domain adaptation under target and conditional shift,” Proc. 29th International Conference on Machine Learning (ICML 2013), Atlanta, USA (full oral presentation; acceptance rate ~ 12%) (link)

  • Kun Zhang and Zhikun Wang, “On estimation of functional causal models: Post-nonlinear causal model as an example,” First IEEE/ICDM workshop on causal discovery, Dallas, USA, Dec., 2013

  • Zhitang Chen, Kun Zhang, and Laiwan Chan, "Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method," 2013 IEEE International Conference on Data Mining (ICDM'13), Dallas, USA, Dec., 2013 (acceptance rate ~ 19.7%)

  • Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij, "Semi-supervised learning in causal and anticausal settings,” in Festschrift for Vladimir Vapnik's 75th birthday, 2013

  • Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniuvsis, Bastian Steudel, Bernhard Schölkopf, “Information-geometric approach to inferring causal directions,” Artificial Intelligence, pp. 1-31, 2012 (link)

  • Zhitang Chen, Kun Zhang and Laiwan Chan, “Causal discovery with scale-mixture model for spatiotemporal variance dependencies,” in Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, Nevada, United States (acceptance rate ~ 25.2%)

  • Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij, "On causal and anticausal learning,” in Proc. 29th International Conference on Machine Learning (ICML 2012), Edinburgh, Scotland, June 2012 (acceptance rate ~ 27.3%) (link)

  • Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, "Kernel-based conditional independence test and application in causal discovery,” in Proc. 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, July 2011 (acceptance rate ~ 30%) (link)

  • Jakob Zscheischler, Dominik Janzing, and Kun Zhang, "Testing whether linear equations are causal: A free probability theory approach,” in Proc. 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, July 2011 (acceptance rate ~ 30%) (link)

  • Kun Zhang and Aapo Hyvärinen, "A general linear non-Gaussian state-space model: Identifiability, identification, and application,” in Proc. 3rd Asian Conference on Machine Learning (ACML 2011), Taoyuan, Taiwan, Nov. 2011 (acceptance rate ~ 38%) (link)

  • Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik Hoyer, "Estimation of a structural vector autoregression model using non-Gaussianity," Journal of Machine Learning Research, 11, pp. 1709-1731, 2010 (link)

  • Kun Zhang and Lai-Wan Chan, "Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion," Neurocomputing, 73(13-15) 2580-2588, 2010

  • Joris Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, and Bernhard Schölkopf, “Probabilistic latent variable models for distinguishing between cause and effect,” in Advances in Neural Information Processing Systems 23, (NIPS 2010), Curran, NY, USA, 1687-1695 (acceptance rate ~ 30%) (link)

  • Kun Zhang, Bernhard Schölkopf, and Dominik Janzing, "Invariant gaussian process latent variable models and application in causal discovery,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (plenary talk, acceptance rate ~ 10%) (link)

  • Kun Zhang and Aapo Hyvärinen, "Source separation and higher-order causal analysis of MEG and EEG,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (acceptance rate ~ 30%) (link)

  • Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf, "Inferring deterministic causal relations,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (plenary talk, acceptance rate ~ 10%) Best Student Paper Award (link)

  • Min-Ling Zhang and Kun Zhang, "Multi-label learning by exploiting label dependency,” in Proc. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD2010), Washington DC, July 2010 (acceptance rate ~ 17%) (link)

  • Kun Zhang and Aapo Hyvärinen, "Distinguishing causes from effects using nonlinear acyclic causal models,” in JMLR Workshop and Conference Proceedings, Volume 6, pp. 157-164, 2010 (presented at NIPS 2008 workshop on causality) Best Benchmark Award (link)

  • Kun Zhang and Lai-Wan Chan, "Efficient factor GARCH models and factor-DCC models," Quantitative Finance , 9(1), pp. 71--91, 2009 (link)

  • Kun Zhang and Aapo Hyvärinen, "Acyclic causality discovery with additive noise: An information-theoretical perspective,” in Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2009, Bled, Slovenia, pp. 570--585, 2009 (acceptance rate ~ 25%) (link)

  • Kun Zhang and Aapo Hyvärinen, "On the identifiability of the post-nonlinear causal model,” in Proc. 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montreal, Canada, 2009 (plenary talk, acceptance rate ~ 10%) (link)

  • Kun Zhang, Heng Peng, Laiwan Chan, and Aapo Hyvärinen, "ICA with sparse connections: Revisited,” in Proc. 8th Int. Conference on Independent Component Analysis and Signal Separation (ICA 2009), Paraty, Brazil, pp. 195--202, 2009 (link)

  • Kun Zhang and Lai-Wan Chan, "Minimal nonlinear distortion principle for nonlinear ICA," Journal of Machine Learning Research, 9, pp. 2455--2487, 2008

  • Kun Zhang and Lai-Wan Chan,"Separating convolutive mixtures by pairwise mutual information minimization,” IEEE Signal Processing Letters, 14(12), pp. 992--995, 2007

  • Wan Zhang, Liu Wenyin, and Kun Zhang, "Symbol recognition with kernel density matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), pp. 2020--2024, 2006

  • Kun Zhang and Lai-Wan Chan, "An adaptive method for subband decomposition ICA,” Neural Computation, 18(1), pp. 191--223, 2006

  • Kun Zhang and Lai-Wan Chan, "Dimension reduction as a deflation method in ICA,” IEEE Signal Processing Letters, 13(1), pp. 45--48, 2006

  • Kun Zhang and Lai-Wan Chan, "Extended Gaussianization method for blind separation of post-nonlinear mixtures,” Neural Computation, 17(2), pp. 425--452, 2006

  • Kun Zhang and Laiwan Chan, "Kernel-based nonlinear independent component analysis,” in Proc. 7th Int. Conference on Independent Component Analysis and Signal Separation (ICA 2007), London, UK, pp. 301--308, Sept., 2007

  • Kun Zhang and Laiwan Chan, "Nonlinear independent component analysis with minimum nonlinear distortion,” the 24th Annual International Conference on Machine Learning (ICML 2007), Corvallis, OR, US, pp. 1127--1134, Jun., 2007

  • Jian Li, Kun Zhang, and Laiwan Chan, "Portfolio construction by reinforcement learning of independent factors,” in Proc. of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), pp. 1020--1031, 2007

  • Kun Zhang and Lai-Wan Chan, "Enhancement of source independence for blind source separation,” in Proc. 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), LNCS 3889, Charleston, SC, USA, pp. 731--738, Mar., 2006

  • Kun Zhang and Lai-Wan Chan, "ICA by PCA approach: relating higher-order statistics to second-order moments,” in Proc. 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), LNCS 3889, Charleston, SC, USA, pp. 311--318, Mar., 2006

  • Kun Zhang and Lai-Wan Chan, "Extensions of ICA for causality discovery in the Hong Kong stock market,” in Proc. 13th International Conference on Neural Information Processing (ICONIP 2006), Hong Kong, Oct., 2006 (link)

  • Kun Zhang and Lai-Wan Chan, "ICA with sparse connections,” in Proc. 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Burgos, Spain, pp. 530--537, Sep., 2006

  • Kun Zhang and Lai-Wan Chan, "To apply score function difference based ICA algorithms to high-dimensional data,” in Proc. of the 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 291--297, 2005

  • Kun Zhang and Lai-Wan Chan, "Practical method for blind inversion of Wiener systems,” in Proc. of the International Joint Conference on Neural Networks (IJCNN'04), Budapest, Hungary, pp. 2163--2168, 2004

  • Kun Zhang and Lai-Wan Chan, "Dimension reduction based on orthogonality -- A decorrelation method in ICA,” in Artificial Neural Networks and Neural Information Processing, LNCS 2714, Springer-Verlag, Istanbul, Turkey, pp. 132--139, 2003




Contact

Email: kunz1(at)cmu.edu
Phone: +1(412)268-8573
Baker Hall 161B
Carnegie Mellon University
5000 Forbes Ave, Pittsburgh, PA 15213