Kun Zhang

发表

Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion

Lai-Wan Chan, Kun Zhang, L. Chan, 2010, Neurocomputing.

Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method

Zhitang Chen, Lai-Wan Chan, Kun Zhang, 2013, 2013 IEEE 13th International Conference on Data Mining.

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Bernhard Schölkopf, Mingming Gong, Dominik Janzing, 2015, ICML.

Kernel-based Conditional Independence Test and Application in Causal Discovery

Bernhard Schölkopf, Dominik Janzing, Kun Zhang, 2011, UAI.

Adaptive Correction of Errors from Recognized Chinese Ink Texts Based on Context

Kun Zhang, Xi-Wen Zhang, Yong-Gang Fu, 2009, 2009 International Conference on Information Technology and Computer Science.

Inferring deterministic causal relations pdf

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2010, UAI.

Discovering Temporal Causal Relations from Subsampled Data

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2015, ICML.

A Permutation-Based Kernel Conditional Independence Test

Bernhard Schölkopf, Krikamol Muandet, Gary Doran, 2014, UAI.

On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example

Bernhard Schölkopf, Zhikun Wang, Kun Zhang, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Semi-supervised Learning in Causal and Anticausal Settings

Bernhard Schölkopf, Joris M. Mooij, Eleni Sgouritsa, 2013, Empirical Inference.

On causal and anticausal learning

Bernhard Schölkopf, Joris M. Mooij, Eleni Sgouritsa, 2012, ICML.

Computational biomechanical analysis of postoperative inferior tibiofibular syndesmosis: a modified modeling method

Kun Zhang, Yijie Zhang, Xiaoyang Jia, 2018, Computer methods in biomechanics and biomedical engineering.

Bridging Information Criteria and Parameter Shrinkage for Model Selection pdf

Lai-Wan Chan, Aapo Hyvärinen, Kun Zhang, 2013, ArXiv.

On Estimation of Functional Causal Models

Bernhard Schölkopf, Zhikun Wang, Kun Zhang, 2015, ACM Trans. Intell. Syst. Technol..

Guest editorial: special issue on causal discovery

Jiuyong Li, Lin Liu, Kun Zhang, 2016, International Journal of Data Science and Analytics.

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data pdf

Clark Glymour, Kun Zhang, Joseph Ramsey, 2019, ArXiv.

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery pdf

Bernhard Schölkopf, Dominik Janzing, Kun Zhang, 2010, UAI.

Research on Visual Comfort Based on Fuzzy Neural Network

Qiang Gao, Kun Zhang, Dahua Li, 2018, 2018 IEEE International Conference on Mechatronics and Automation (ICMA).

Towards Robust and Specific Causal Discovery from fMRI pdf

Bernhard Schölkopf, Dajiang Zhu, Kun Zhang, 2015, ArXiv.

Domain Adaptation under Target and Conditional Shift

Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, 2013, ICML.

Robust Learning via Cause-Effect Models pdf

Bernhard Schölkopf, Dominik Janzing, Kun Zhang, 2011, ArXiv.

Information-geometric approach to inferring causal directions

Bernhard Schölkopf, Joris M. Mooij, Jan Lemeire, 2012, Artif. Intell..

Multi-Source Domain Adaptation: A Causal View

Bernhard Schölkopf, Mingming Gong, Kun Zhang, 2015, AAAI.

Testing whether linear equations are causal: A free probability theory approach pdf

Dominik Janzing, Kun Zhang, Jakob Zscheischler, 2011, UAI.

Probabilistic latent variable models for distinguishing between cause and effect

Bernhard Schölkopf, Joris M. Mooij, Oliver Stegle, 2010, NIPS.

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2016, UAI.

Identification of Time-Dependent Causal Model: A Gaussian Process Treatment

Bernhard Schölkopf, Kun Zhang, Biwei Huang, 2015, IJCAI.

Distinguishing Cause from Effect Based on Exogeneity pdf

Bernhard Schölkopf, Kun Zhang, Jiji Zhang, 2015, ArXiv.

Generalized Score Functions for Causal Discovery

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, KDD.

Information Diffusion Temporal Dynamic Prediction in Microblog System Based On User Influence Learning

Hong Zhang, Kun Zhang, Fawang Han, 2016 .

A New Measure of Conditional Dependence

Kun Zhang, Negar Kiyavash, Jalal Etesami, 2017, 1704.00607.

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2017, IJCAI.

Domain Adaptation with Conditional Transferable Components

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2016, ICML.

Causal Generative Domain Adaptation Networks pdf

Dacheng Tao, Mingming Gong, Clark Glymour, 2018, ArXiv.

Causal Discovery from Temporally Aggregated Time Series

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2017, UAI.

Preface to the ACM TIST Special Issue on Causal Discovery and Inference

Bernhard Schölkopf, Jiuyong Li, Kun Zhang, 2016, ACM Trans. Intell. Syst. Technol..

Causal Discovery via Reproducing Kernel Hilbert Space Embeddings

Bernhard Schölkopf, Zhitang Chen, Lai-Wan Chan, 2014, Neural Computation.

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions pdf

Mingming Gong, Clark Glymour, Kun Zhang, 2017, ArXiv.

Causal discovery with scale-mixture model for spatiotemporal variance dependencies

Zhitang Chen, Lai-Wan Chan, Kun Zhang, 2012, NIPS.

Improving Causal Discovery By Optimal Bayesian Network Learning

Changhe Yuan, Kun Zhang, Ni Y. Lu, 2021, AAAI.

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders pdf

Ruichu Cai, Clark Glymour, Zhifeng Hao, 2021, ArXiv.

Testing Independence Between Linear Combinations for Causal Discovery

Jihong Guan, Kun Zhang, Shuigeng Zhou, 2021, AAAI.

Nonlinear Functional Causal Models for Distinguishing Cause from Effect

Aapo Hyvärinen, Kun Zhang, Kun Zhang, 2016 .

Learning causality and causality-related learning: some recent progress.

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, National science review.