Kun Zhang 0001

94 publications

20 venues

H Index 32

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
Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI 2022 4
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. CVPR 2022 6
Conditional Contrastive Learning with Kernel. ICLR 2022 8
Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. AAAI 2022 1
Adversarial Robustness Through the Lens of Causality. ICLR 2022 20
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. CVPR 2022 1
Partial disentanglement for domain adaptation. ICML 2022 5
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022 10
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. AISTATS 2022 0
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS 2022 0
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022 0
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR 2022 0
Learning Temporally Causal Latent Processes from General Temporal Data. ICLR 2022 0
Instance-dependent Label-noise Learning under a Structural Causal Model. NIPS/NeurIPS 2021 16
Progressive Open-Domain Response Generation with Multiple Controllable Attributes. IJCAI 2021 8
Unaligned Image-to-Image Translation by Learning to Reweight. ICCV 2021 7
Improving Causal Discovery By Optimal Bayesian Network Learning. AAAI 2021 5
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NIPS/NeurIPS 2021 15
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. NIPS/NeurIPS 2021 12
Testing Independence Between Linear Combinations for Causal Discovery. AAAI 2021 6
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI 2021 16
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NIPS/NeurIPS 2021 0
A Causal View on Robustness of Neural Networks. NIPS/NeurIPS 2020 45
Compressed Self-Attention for Deep Metric Learning. AAAI 2020 4
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NIPS/NeurIPS 2020 71
Label-Noise Robust Domain Adaptation. ICML 2020 21
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020 17
Domain Adaptation as a Problem of Inference on Graphical Models. NIPS/NeurIPS 2020 40
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NIPS/NeurIPS 2020 21
How do fair decisions fare in long-term qualification? NIPS/NeurIPS 2020 39
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020 14
Adaptive Task Sampling for Meta-learning. ECCV 2020 24
Generative-Discriminative Complementary Learning. AAAI 2020 0
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML 2020 0
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. JMLR 2020 0
Causal Discovery from Heterogeneous/Nonstationary Data. JMLR 2020 0
Twin Auxilary Classifiers GAN. NIPS/NeurIPS 2019 30
Domain Generalization via Multidomain Discriminant Analysis. UAI 2019 45
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. NIPS/NeurIPS 2019 3
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019 50
Triad Constraints for Learning Causal Structure of Latent Variables. NIPS/NeurIPS 2019 20
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019 32
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019 18
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NIPS/NeurIPS 2019 15
On Learning Invariant Representations for Domain Adaptation. ICML 2019 262
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NIPS/NeurIPS 2019 19
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019 13
PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network. CIKM 2019 4
Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019 63
Causal Discovery in the Presence of Missing Data. AISTATS 2019 0
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019 0
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. CVPR 2019 0
Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019 0
Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. AAAI 2018 4
Deep Domain Generalization via Conditional Invariant Adversarial Networks. ECCV 2018 380
Causal Discovery from Discrete Data using Hidden Compact Representation. NIPS/NeurIPS 2018 25
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. NIPS/NeurIPS 2018 41
Multi-domain Causal Structure Learning in Linear Systems. NIPS/NeurIPS 2018 29
Generalized Score Functions for Causal Discovery. KDD 2018 66
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018 13
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018 0
Causal Discovery Using Regression-Based Conditional Independence Tests. AAAI 2017 23
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 40
Learning Causal Structures Using Regression Invariance. NIPS/NeurIPS 2017 37
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017 99
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017 25
Domain Adaptation with Conditional Transferable Components. ICML 2016 283
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. UAI 2016 59
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 10
Multi-Source Domain Adaptation: A Causal View. AAAI 2015 157
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015 33
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015 68
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015 0
A Permutation-Based Kernel Conditional Independence Test. UAI 2014 89
Domain Adaptation under Target and Conditional Shift. ICML 2013 487
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. ICDM 2013 17
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. NIPS/NeurIPS 2012 6
On causal and anticausal learning. ICML 2012 424
Information-geometric approach to inferring causal directions. Artificial Intelligence 2012 250
A General Linear Non-Gaussian State-Space Model. ACML 2011 14
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 441
Multi-label learning by exploiting label dependency. KDD 2010 425
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. JMLR 2010 241
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010 13
Inferring deterministic causal relations. UAI 2010 156
Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI 2010 17
Probabilistic latent variable models for distinguishing between cause and effect. NIPS/NeurIPS 2010 119
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD 2009 36
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009 394
Nonlinear independent component analysis with minimal nonlinear distortion. ICML 2007 9
Symbol Recognition with Kernel Density Matching. TPAMI 2006 54
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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