Kun Zhang 0001

55 publications

16 venues

H Index19


Max Planck Institute for Intelligent Systems, Tübingen, Germany
Chinese University of Hong Kong, Hong Kong
Carnegie Mellon University, Department of Philosophy, Pittsburgh


Name Venue Year Citations
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015 19
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. NIPS/NeurIPS 2018 7
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009 173
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015 43
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019 3
Multi-domain Causal Structure Learning in Linear Systems. NIPS/NeurIPS 2018 5
Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019 3
Generalized Score Functions for Causal Discovery. KDD 2018 8
Multi-label learning by exploiting label dependency. KDD 2010 259
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NIPS/NeurIPS 2019 0
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019 2
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD 2009 29
Inferring deterministic causal relations. UAI 2010 92
Learning Causal Structures Using Regression Invariance. NIPS/NeurIPS 2017 9
Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA. ICANN 2003 5
A General Linear Non-Gaussian State-Space Model. ACML 2011 6
On causal and anticausal learning. ICML 2012 142
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. JMLR 2010 109
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010 10
Multi-Source Domain Adaptation: A Causal View. AAAI 2015 55
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. UAI 2016 27
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 8
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 14
Triad Constraints for Learning Causal Structure of Latent Variables. NIPS/NeurIPS 2019 0
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018 3
Domain Adaptation under Target and Conditional Shift. ICML 2013 189
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019 2
Deep Domain Generalization via Conditional Invariant Adversarial Networks. ECCV 2018 34
Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. AAAI 2018 7
Domain Adaptation with Conditional Transferable Components. ICML 2016 103
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011 199
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. ICDM 2013 12
Probabilistic latent variable models for distinguishing between cause and effect. NIPS/NeurIPS 2010 70
A Permutation-Based Kernel Conditional Independence Test. UAI 2014 37
Testing whether linear equations are causal: A free probability theory approach. UAI 2011 22
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NIPS/NeurIPS 2019 0
Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI 2010 15
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. NIPS/NeurIPS 2012 6
Causal Discovery from Discrete Data using Hidden Compact Representation. NIPS/NeurIPS 2018 1
Causal Discovery Using Regression-Based Conditional Independence Tests. AAAI 2017 2
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015 35
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017 25
Information-geometric approach to inferring causal directions. Artificial Intelligence 2012 119
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017 11
On Learning Invariant Representations for Domain Adaptation. ICML 2019 7
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019 6
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019 1
Twin Auxilary Classifiers GAN. NIPS/NeurIPS 2019 2
Causal Discovery in the Presence of Missing Data. AISTATS 2019 4
To apply score function difference based ICA algorithms to high-dimensional data. ESANN 2005 0
Nonlinear independent component analysis with minimal nonlinear distortion. ICML 2007 7
Domain Generalization via Multidomain Discriminant Analysis. UAI 2019 1
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. CVPR 2019 0
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018 0
Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019 0
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