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Dr. Kun Zhang |
| Address: | Spemannstr. 38 72076 Tübingen |
| Room number: | 208 |
| Phone: | +49 7071 601-563 |
| Fax: | +49 7071 601 552 |
| E-Mail: |  kzhang |
There has been a long history of debate on causality in philosophy, statistics, economics, and related fields. I have been concerned with this classic question—how can we discover causal information from purely observed data (i.e., perform causal inference)? How such causal information can facilitate solving other problems such as modeling, prediction, and control, is also interesting to me.
My research consists of three main lines. Firstly, I have focused on developing practical and advanced computational methods for causal inference, to produce more reliable causal information. Secondly, to better understand causality and derive more universal methods for causal inference, I also work on finding fundamental and testable principles that help discover causality from data. Thirdly, latent variable modeling is closely related to causality, and it has been interesting me for over eight years. Developing more general yet identifiable latent variable models would benefit the causality field, as well as the machine learning and signal processing communities. Since machine learning plays a key role in data analysis as well as causal inference, I would also like to contribute to this field.
We are currently organizing the workshop titled "Causality: Perspectives from different disciplines".
*** We are currently organizing the workshop titled "Causality: Perspectives from different disciplines". ***
Ongoing projects:
- fundamental characterization of causal influence in observed data, and refinement of concepts related to causality
- precise notion of “model complexity” for causal inference
- unified/universal approach for causal inference
- domain-specific causal inference (in finance, brain signal analysis, etc.)
- causal understanding of machine learning tasks
- practical causal inference system for large-scale problems
- domain adaptation
- computational finance
Research Interests
· Causal discovery: Theory and applications
o developing advanced and practical computational methods for causal inference
o finding fundamental and testable principles to characterize causality
o latent variable modeling
· Statistical machine learning and applications
o kernel methods, Gaussian processes, mixture models, model selection, independent component analysis, sparse coding
· Computational finance
· Neuroscience (especially MEG and EEG data analysis)
Academic Service
· Organizational activities
o Organizer of workshop "Causality: Perspectives from different disciplines," Vals, Switzerland, August 5-8, 2013
o Co-organizer of the First IEEE ICDM Workshop on Causal Discovery (CD 2013), Dallas, Texas, USA, December 8, 2013
o Co-organizer of workshop “Networks -- Processes and causality”, Menorca, Spain, September, 2012
o Publicity chair of AISTATS 2012 (15th International Conference on Artificial Intelligence and Statistics)
o Organizer and chair of special session on Independent Component Analysis at ICONIP 2006 (13th International Conference on Neural Information Processing)
· Reviewer for grant proposals
o US National Science Foundation (NSF), 2010, 2011 & 2012;
o Hong Kong Research Grants Council (RGC), 2011, 2012 & 2013;
o Netherlands Organization for Scientific Research (NWO), 2012
· Reviewer for journals
o Annals of Statistics; Journal of Machine Learning Research; Annals of Applied Statistics; Neural Computation; Machine Learning; IEEE Transactions on Neural Networks; IEEE Transactions on Signal Processing; Neural Networks; IEEE Transactions on Knowledge and Data Engineering; Quantitative Finance; Neurocomputing; IEEE Signal Processing Letters; Frontiers of Computer Science; International Journal of Imaging Systems and Technology; Circuits, Systems & Signal Processing; International Review of Economics and Finance
· Program committee member for international conferences
o 2013: UAI, NIPS, AISTATS, SDM, KDD, IJCAI, IJCNN, ASE/IEEE Big Data…
o 2012: UAI, AISTATS, MLSP, WSDM, SDM;
o 2011: UAI, NIPS, KDD, IJCNN, ICONIP;
o 2010: UAI, NIPS, ICA/LVA, SDM, ACML, ICPR;
o 2009: NIPS, ACML, ICONIP;
Articles (10): | |
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Janzing D  , Mooij J , Zhang K , , Zscheischler J , Daniušis P , Steudel B and Schölkopf B (2012) Information-geometric approach to inferring causal directions
Artificial Intelligence 182-183 1-31. 
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Zhang K and (2010) Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion
Neurocomputing 73(13-15) 2580-2588.
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, Zhang K , and (2010) Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Journal of Machine Learning Research 11 1709-1731.
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Zhang K and (2009) Efficient factor GARCH models and factor-DCC models
Quantitative Finance 9(1) 71-91.
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Zhang K and (2008) Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis
Journal of Machine Learning Research 9 2455-2487.
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Zhang K and (2007) Separating convolutive mixtures by pairwise mutual information minimization", IEEE Signal Processing Letters
IEEE Signal Processing Letters 14(12) 992-995.
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Zhang K and (2006) An adaptive method for subband decomposition ICA
Neural Computation 18(1) 191-223.
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Zhang K and (2006) Dimension Reduction as a Deflation Method in ICA
IEEE Signal Processing Letters 13(1) 45-48.
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, and Zhang K (2006) Symbol Recognition with Kernel Density Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12) 2020-2024.
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Zhang K and (2005) Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures
Neural Computation 17(2) 425-452.
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Conference papers (25): | |
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, Zhang K and (2013) Causal discovery with scale-mixture model for spatiotemporal variance dependencies
In: Advances in Neural Information Processing Systems 25, (Ed) , , , and , 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Curran Associates Inc., 1736--1744.
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Zhang K , Schölkopf B , Muandet K and Wang Z (2013) Domain adaptation under Target and Conditional Shift
30th International Conference on Machine Learning (ICML 2013).
accepted
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Schölkopf B , Janzing D , Peters J , Sgouritsa E , Zhang K and Mooij J (2012) On Causal and Anticausal Learning
In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), (Ed) and , 29th International Conference on Machine Learning (ICML 2012), Omnipress, New York, NY, USA, 1255-1262.
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Zhang K and (2011) A general linear non-Gaussian state-space model: Identifiability, identification, and applications
In: JMLR Workshop and Conference Proceedings Volume 20, (Ed) Hsu, C.-N. , W.S. Lee, 3rd Asian Conference on Machine Learning (ACML 2011), MIT Press, Cambridge, MA, USA, 113-128.
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Zhang K , Peters J , Janzing D and Schölkopf B (2011) Kernel-based Conditional Independence Test and Application in
Causal Discovery
(Ed) and , 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 804-813.
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Zscheischler J , Janzing D and Zhang K (2011) Testing whether linear equations are causal: A free probability theory approach
(Ed) Cozman, F.G. , A. Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 839-847.
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Daniusis P , Janzing D , Mooij J , Zscheischler J , Steudel B , Zhang K and Schölkopf B (2010) Inferring deterministic causal relations
In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) and , UAI 2010, AUAI Press, Corvallis, OR, USA, 143-150.
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Zhang K , Schölkopf B and Janzing D (2010) Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) and , UAI 2010, AUAI Press, Corvallis, OR, USA, 717-724.
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and Zhang K (2010) Multi-Label Learning by Exploiting Label Dependency
(Ed) Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang, 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), ACM Press, New York, NY, USA, 999-1008.
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Zhang K and (2010) Source Separation and Higher-Order Causal Analysis of MEG and EEG
(Ed) Grünwald, P. , P. Spirtes, 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), AUAI Press, Corvallis, OR, USA, 709-716.
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Zhang K and (2010) Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models
In: JMLR Workshop and Conference Proceedings, Volume 6, (Ed) , and , Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 157-164.
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Mooij JM , Stegle O , Janzing D , Zhang K and Schölkopf B (2010) Probabilistic latent variable models for distinguishing between cause and effect
In: Advances in Neural Information Processing Systems 23, (Ed) , , , and , 24th Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 1687-1695.
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Zhang K and (2009) Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
In: Machine Learning and Knowledge Discovery in Databases, (Ed) Buntine, W. , M. Grobelnik, D. Mladenić, J. Shawe-Taylor, European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (ECML PKDD '09), Springer, Berlin, Germany, 570-585.
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Zhang K and (2009) On the Identifiability of the Post-Nonlinear Causal Model
(Ed) Bilmes, J. , A. Y. Ng, D. A. McAllester, 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, Corvallis, OR, USA, 647-655.
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Zhang K , , and (2009) ICA with Sparse Connections: Revisited
In: Independent Component Analysis and Signal Separation, (Ed) Adali, T. , Christian Jutten, J.M. Travassos Romano, A. Kardec Barros, 8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009), Springer, Berlin, Germany, 195-202.
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Zhang K and (2007) Nonlinear independent component analysis with minimum nonlinear distortion
In: ICML '07: Proceedings of the 24th international conference on Machine learning, (Ed) Z Ghahramani, 24th International Conference on Machine Learning (ICML 2007), ACM, New York, NY, USA, 1127-1134.
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, Zhang K and (2007) Independent Factor Reinforcement Learning for Portfolio Management
In: Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), (Ed) , , , and , 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), Springer, Berlin, Germany, 1020-1031.
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Zhang K and (2007) Kernel-Based Nonlinear Independent Component Analysis
In: Independent Component Analysis and Signal Separation, 7th International Conference, ICA 2007, (Ed) , , and , 7th International Conference on Independent Component Analysis and Signal Separation (ICA 2007), Springer, 301-308.
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Zhang K and (2006) ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments
In: Independent Component Analysis and Blind Signal Separation, (Ed) , , and , 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), Springer, 311-318.
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Zhang K and (2006) Enhancement of source independence for blind source separation
In: Independent Component Analysis and Blind Signal Separation, LNCS 3889, (Ed) , and , 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), Springer, Berlin, Germany, 731-738.
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Zhang K and (2006) Extensions of ICA for Causality Discovery in the Hong Kong Stock Market
In: Neural Information Processing, 13th International Conference, ICONIP 2006, (Ed) , , and , 13th International Conference on Neural Information Processing (ICONIP 2006), Springer, 400-409.
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Zhang K and (2006) ICA with Sparse Connections
In: Intelligent Data Engineering and Automated Learning – IDEAL 2006, (Ed) , and , 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Springer, 530-537.
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Zhang K and (2005) To apply score function difference based ICA algorithms to high-dimensional data
In: Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), 13th European Symposium on Artificial Neural Networks (ESANN 2005), 291-297.
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Zhang K and (2004) Practical Method for Blind Inversion of Wiener Systems
In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), International Joint Conference on Neural Networks (IJCNN 2004), 2163-2168.
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Zhang K and (2003) Dimension Reduction Based on Orthogonality — a Decorrelation Method in ICA
In: Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, (Ed) , , and , International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, Springer, Berlin, Germany, 132-139.
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