Contact

Dr. Kun Zhang

Address: Spemannstr. 38
72076 Tübingen
Room number: 208
Phone: +49 7071 601-563
Fax: +49 7071 601 552
E-Mail: kzhang
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Kun Zhang

Position: Senior Research Scientist  Unit: Schölkopf

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 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 am also very interested in this field.

 

*What's new*

--  The workshop "Causal modeling & machine learning" will take place in Beijing, China, in June 2014.

--  We are editing the ACM Transactions on Intelligent Systems and Technologies (ACM TIST) special issue on causal discovery and inference; see the call for papers here.  Submission deadline: 14 March 2014.

--  The workshop "Causality: Perspectives from different disciplines" took place in August, 2013.

-- Slides and poster for a recent paper "Domain adaptation under target and conditional shift."

Ongoing projects:

- fundamental characterization of causal information in observational 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, domain adaptation, 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 ICML'14 workshop "Causal modeling and machine learning" (with Bernhard Schölkopf, Elias Bareinboim, and Jiji Zhang), June, 2014

o Guest editor of ACM Transactions on Intelligent Systems and Technologie special issue on Causality (with Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, and Judea Pearl)

o Organizer of workshop "Causality: Perspectives from different disciplines" (with Bernhard Schölkopf and Jiji Zhang), 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)

· Reviewer for journals

o Annals of Statistics; Journal of Machine Learning Research; Annals of Applied Statistics; Journal of the American Statistical Association; Neural Computation; Machine Learning; IEEE Transactions on Pattern Analysis and Machine Intelligence; 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 2014: AISTATS (senior program committee), UAI, NIPS, WSDM, KDD, iKDD CoDS...

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

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Articles (12):

Chen Z Person, Zhang K Person, Chan L and Schölkopf B Person (2014) Causal discovery via reproducing kernel Hilbert space embeddings Neural Computation . accepted
Wang Z Person, Deisenroth M Person, Zhang K Person, Boularias A Person, Schölkopf B Person and Peters J Person (2013) Hierarchical Gaussian Process Dynamics Models for Human Movement Analysis . submitted
Janzing D Person, Mooij J Person, Zhang K Person, Lemeire J , Zscheischler J Person, Daniušis P Person, Steudel B Person and Schölkopf B Person (2012) Information-geometric approach to inferring causal directions Artificial Intelligence 182-183 1-31.
Zhang K Person and Chan L-W (2010) Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion Neurocomputing 73(13-15) 2580-2588.
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Hyvärinen A , Zhang K Person, Shimizu S and Hoyer P (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 Person and Chan L (2009) Efficient factor GARCH models and factor-DCC models Quantitative Finance 9(1) 71-91.
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Zhang K Person and Chan L (2008) Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis Journal of Machine Learning Research 9 2455-2487.
Zhang K Person and Chan L (2007) Separating convolutive mixtures by pairwise mutual information minimization", IEEE Signal Processing Letters IEEE Signal Processing Letters 14(12) 992-995.
Zhang K Person and Chan L (2006) An adaptive method for subband decomposition ICA Neural Computation 18(1) 191-223.
Zhang K Person and Chan L (2006) Dimension Reduction as a Deflation Method in ICA IEEE Signal Processing Letters 13(1) 45-48.
Zhang W , Wenyin L and Zhang K Person (2006) Symbol Recognition with Kernel Density Matching IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12) 2020-2024.
Zhang K Person and Chan L (2005) Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures Neural Computation 17(2) 425-452.

Conference papers (28):

Doran G Person, Muandet K Person, Zhang K Person and Schölkopf B Person (2014) A Permutation-Based Kernel Conditional Independence Test In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), (Ed) Nevin L. Zhang and Jin Tian, UAI2014, AUAI Press Corvallis, Oregon, 132--141.
Zhang K Person, Schölkopf B Person, Muandet K Person and Wang Z Person (2013) Domain adaptation under Target and Conditional Shift In: Proceedings of The 30th International Conference on Machine Learning, Volume 28, (Ed) Sanjoy Dasgupta and David McAllester, 30th International Conference on Machine Learning (ICML 2013), 819–827.
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Chen Z Person, Zhang K Person and Chan L (2013) Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method IEEE International Conference on Data Mining (ICDM'13), IEEE Computer Society.
Zhang K Person, Wang Z Person and Schölkopf B Person (2013) On estimation of functional causal models: Post-nonlinear causal model as an example First IEEE ICDM workshop on causal discovery.
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Chen Z , Zhang K Person and Chan L (2012) Causal discovery with scale-mixture model for spatiotemporal variance dependencies In: Advances in Neural Information Processing Systems 25, (Ed) P Bartlett, FCN Pereira, CJC. Burges, L Bottou and KQ Weinberger, 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Curran Associates Inc., 1736--1744.
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Schölkopf B Person, Janzing D Person, Peters J Person, Sgouritsa E Person, Zhang K Person and Mooij J Person (2012) On Causal and Anticausal Learning In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), (Ed) J Langford and J Pineau, 29th International Conference on Machine Learning (ICML 2012), Omnipress, New York, NY, USA, 1255-1262.
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Zhang K Person and Hyvärinen A (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 Person, Peters J Person, Janzing D Person and Schölkopf B Person (2011) Kernel-based Conditional Independence Test and Application in Causal Discovery (Ed) FG Cozman and A Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 804-813.
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Zscheischler J Person, Janzing D Person and Zhang K Person (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 Person, Janzing D Person, Mooij J Person, Zscheischler J Person, Steudel B Person, Zhang K Person and Schölkopf B Person (2010) Inferring deterministic causal relations In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, UAI 2010, AUAI Press, Corvallis, OR, USA, 143-150.
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Zhang K Person, Schölkopf B Person and Janzing D Person (2010) Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, UAI 2010, AUAI Press, Corvallis, OR, USA, 717-724.
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Zhang M-L and Zhang K Person (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 Person and Hyvärinen A (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 Person and Hyvärinen A (2010) Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models In: JMLR Workshop and Conference Proceedings, Volume 6, (Ed) I Guyon, D Janzing and B Schölkopf, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 157-164.
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Mooij JM Person, Stegle O Person, Janzing D Person, Zhang K Person and Schölkopf B Person (2010) Probabilistic latent variable models for distinguishing between cause and effect In: Advances in Neural Information Processing Systems 23, (Ed) J Lafferty, CKI Williams, J Shawe-Taylor, RS Zemel and A Culotta, 24th Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 1687-1695.
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Zhang K Person and Hyvärinen A (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 Person and Hyvärinen A (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 Person, Peng H , Chan L and Hyvärinen A (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 Person and Chan L (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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Li J , Zhang K Person and Chan L (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) H Yin, P Tiño, E Corchado, W Byrne and X Yao, 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), Springer, Berlin, Germany, 1020-1031.
Zhang K Person and Chan L (2007) Kernel-Based Nonlinear Independent Component Analysis In: Independent Component Analysis and Signal Separation, 7th International Conference, ICA 2007, (Ed) M E Davies, C J James, S A Abdallah and M D Plumbley, 7th International Conference on Independent Component Analysis and Signal Separation (ICA 2007), Springer, 301-308.
Zhang K Person and Chan L (2006) ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments In: Independent Component Analysis and Blind Signal Separation, (Ed) J P Rosca, D Erdogmus, J C Príncipe and S Haykin, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), Springer, 311-318.
Zhang K Person and Chan L (2006) Enhancement of source independence for blind source separation In: Independent Component Analysis and Blind Signal Separation, LNCS 3889, (Ed) J. Rosca, D. Erdogmus and JC Príncipe und S. Haykin, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), Springer, Berlin, Germany, 731-738.
Zhang K Person and Chan L (2006) Extensions of ICA for Causality Discovery in the Hong Kong Stock Market In: Neural Information Processing, 13th International Conference, ICONIP 2006, (Ed) I King, J Wang, L Chan and D L Wang, 13th International Conference on Neural Information Processing (ICONIP 2006), Springer, 400-409.
Zhang K Person and Chan L (2006) ICA with Sparse Connections In: Intelligent Data Engineering and Automated Learning – IDEAL 2006, (Ed) E Corchado, H Yin and V Botti und Colin Fyfe, 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Springer, 530-537.
Zhang K Person and Chan L (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 Person and Chan L (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.
Zhang K Person and Chan L (2003) Dimension Reduction Based on Orthogonality — a Decorrelation Method in ICA In: Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, (Ed) O Kaynak, E Alpaydin, E Oja and L Xu, International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, Springer, Berlin, Germany, 132-139.

Contributions to books (1):

Schölkopf B Person, Janzing D Person, Peters J Person, Sgouritsa E Person, Zhang K Person and Mooij J Person: Semi-supervised learning in causal and anticausal settings, 129--141. In: Empirical Inference, (Ed) Z Luo B Schölkopf and V Vovk, Springer-Verlag, (2013).

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