Kun Zhang 0001
94 publications
20 venues
H Index 32
Affiliation
Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USAMax 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.
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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.
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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.
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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 |