Kun Zhang 0001

55 publications

16 venues

H Index19

Affiliation

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

Links

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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