Kun Zhang 0001

74 publications

17 venues

H Index 28

Affiliation

Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA
Max Planck Institute for Intelligent Systems, T bingen, Germany
Chinese University of Hong Kong, Hong Kong

Links

Name Venue Year citations
Unaligned Image-to-Image Translation by Learning to Reweight. ICCV 2021 4
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI 2021 2
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NIPS/NeurIPS 2021 3
Testing Independence Between Linear Combinations for Causal Discovery. AAAI 2021 1
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NIPS/NeurIPS 2021 0
Instance-dependent Label-noise Learning under a Structural Causal Model. NIPS/NeurIPS 2021 0
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020 5
Compressed Self-Attention for Deep Metric Learning. AAAI 2020 4
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. JMLR 2020 8
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NIPS/NeurIPS 2020 26
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NIPS/NeurIPS 2020 5
How do fair decisions fare in long-term qualification? NIPS/NeurIPS 2020 16
Label-Noise Robust Domain Adaptation. ICML 2020 17
Domain Adaptation as a Problem of Inference on Graphical Models. NIPS/NeurIPS 2020 25
A Causal View on Robustness of Neural Networks. NIPS/NeurIPS 2020 24
Generative-Discriminative Complementary Learning. AAAI 2020 13
Causal Discovery from Heterogeneous/Nonstationary Data. JMLR 2020 39
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020 8
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. CVPR 2019 74
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019 12
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019 30
Domain Generalization via Multidomain Discriminant Analysis. UAI 2019 27
Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019 41
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019 23
Twin Auxilary Classifiers GAN. NIPS/NeurIPS 2019 25
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019 10
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NIPS/NeurIPS 2019 10
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019 14
Triad Constraints for Learning Causal Structure of Latent Variables. NIPS/NeurIPS 2019 10
Causal Discovery in the Presence of Missing Data. AISTATS 2019 24
On Learning Invariant Representations for Domain Adaptation. ICML 2019 160
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NIPS/NeurIPS 2019 13
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. NIPS/NeurIPS 2019 1
Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019 0
Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. AAAI 2018 33
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018 1
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018 9
Generalized Score Functions for Causal Discovery. KDD 2018 36
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. NIPS/NeurIPS 2018 30
Deep Domain Generalization via Conditional Invariant Adversarial Networks. ECCV 2018 244
Causal Discovery from Discrete Data using Hidden Compact Representation. NIPS/NeurIPS 2018 17
Multi-domain Causal Structure Learning in Linear Systems. NIPS/NeurIPS 2018 15
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 32
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017 17
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017 66
Learning Causal Structures Using Regression Invariance. NIPS/NeurIPS 2017 28
Causal Discovery Using Regression-Based Conditional Independence Tests. AAAI 2017 15
Domain Adaptation with Conditional Transferable Components. ICML 2016 239
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 9
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. UAI 2016 51
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015 27
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015 57
Multi-Source Domain Adaptation: A Causal View. AAAI 2015 128
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015 60
A Permutation-Based Kernel Conditional Independence Test. UAI 2014 71
Domain Adaptation under Target and Conditional Shift. ICML 2013 398
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. ICDM 2013 15
Information-geometric approach to inferring causal directions. Artificial Intelligence 2012 212
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. NIPS/NeurIPS 2012 6
On causal and anticausal learning. ICML 2012 321
Testing whether linear equations are causal: A free probability theory approach. UAI 2011 36
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011 359
A General Linear Non-Gaussian State-Space Model. ACML 2011 12
Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI 2010 16
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. JMLR 2010 208
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010 13
Probabilistic latent variable models for distinguishing between cause and effect. NIPS/NeurIPS 2010 109
Inferring deterministic causal relations. UAI 2010 139
Multi-label learning by exploiting label dependency. KDD 2010 395
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009 321
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD 2009 33
Nonlinear independent component analysis with minimal nonlinear distortion. ICML 2007 9
To apply score function difference based ICA algorithms to high-dimensional data. ESANN 2005 0
Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA. ICANN 2003 6
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