Kun Zhang

Orcid: 0000-0002-0738-9958

Affiliations:
  • Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA
  • Max Planck Institute for Intelligent Systems, Tübingen, Germany
  • Chinese University of Hong Kong, Hong Kong (PhD 2005)


According to our database1, Kun Zhang authored at least 253 papers between 2003 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Online presence:

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Bibliography

2024
Graph Domain Adaptation: A Generative View.
ACM Trans. Knowl. Discov. Data, April, 2024

Transferable Time-Series Forecasting Under Causal Conditional Shift.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2024

Counterfactual Generation with Identifiability Guarantees.
CoRR, 2024

When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting.
CoRR, 2024

Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models.
CoRR, 2024

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models.
CoRR, 2024

Causal Representation Learning from Multiple Distributions: A General Setting.
CoRR, 2024

Discovery of the Hidden World with Large Language Models.
CoRR, 2024

Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models.
CoRR, 2024

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process.
CoRR, 2024

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization.
CoRR, 2024

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors.
CoRR, 2024

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Contextual-Assisted Scratched Photo Restoration.
IEEE Trans. Circuits Syst. Video Technol., October, 2023

Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions.
Neurocomputing, March, 2023

Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations.
IEEE Trans. Neural Networks Learn. Syst., February, 2023

Conditional Independence Test Based on Residual Similarity.
ACM Trans. Knowl. Discov. Data, 2023

Editorial Special Issue on Causality: Fundamental Limits and Applications.
IEEE J. Sel. Areas Inf. Theory, 2023

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective.
ACM Comput. Surv., 2023

Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction.
CoRR, 2023

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables.
CoRR, 2023

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation.
CoRR, 2023

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models.
CoRR, 2023

Procedural Fairness Through Decoupling Objectionable Data Generating Components.
CoRR, 2023

Identifying Semantic Component for Robust Molecular Property Prediction.
CoRR, 2023

Generalizing Nonlinear ICA Beyond Structural Sparsity.
CoRR, 2023

Identifiable Latent Polynomial Causal Models Through the Lens of Change.
CoRR, 2023

Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment.
CoRR, 2023

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables.
CoRR, 2023

Causal-learn: Causal Discovery in Python.
CoRR, 2023

Learning World Models with Identifiable Factorization.
CoRR, 2023

Partial Identifiability for Domain Adaptation.
CoRR, 2023

Advancing Counterfactual Inference through Quantile Regression.
CoRR, 2023

Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data.
CoRR, 2023

Voices of Her: Analyzing Gender Differences in the AI Publication World.
CoRR, 2023

Explainable Recommender with Geometric Information Bottleneck.
CoRR, 2023

Structure Learning with Continuous Optimization: A Sober Look and Beyond.
CoRR, 2023

Beware of Instantaneous Dependence in Reinforcement Learning.
CoRR, 2023

On the Opportunity of Causal Deep Generative Models: A Survey and Future Directions.
CoRR, 2023

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data.
CoRR, 2023

Increasing Fairness in Compromise on Accuracy via Weighted Vote with Learning Guarantees.
CoRR, 2023

Counterfactual Generation with Identifiability Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Temporally Disentangled Representation Learning under Unknown Nonstationarity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning World Models with Identifiable Factorization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Identification of Nonlinear Latent Hierarchical Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Subspace Identification for Multi-Source Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
Proceedings of the International Conference on Machine Learning, 2023

Feature Expansion for Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023

Identifiability of Label Noise Transition Matrix.
Proceedings of the International Conference on Machine Learning, 2023

Model Transferability with Responsive Decision Subjects.
Proceedings of the International Conference on Machine Learning, 2023

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants.
Proceedings of the International Conference on Machine Learning, 2023

Evolving Semantic Prototype Improves Generative Zero-Shot Learning.
Proceedings of the International Conference on Machine Learning, 2023

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Multi-domain image generation and translation with identifiability guarantees.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Causal Balancing for Domain Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

GAIN: On the Generalization of Instructional Action Understanding.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Unpaired Image-to-Image Translation with Shortest Path Regularization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Understanding Masked Autoencoders via Hierarchical Latent Variable Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Scalable Causal Discovery with Score Matching.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.
IEEE Trans. Neural Networks Learn. Syst., 2022

Relevance attack on detectors.
Pattern Recognit., 2022

Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models.
Entropy, 2022

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks.
CoRR, 2022

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations.
CoRR, 2022

Prompt Learning with Optimal Transport for Vision-Language Models.
CoRR, 2022

Identifying Latent Causal Content for Multi-Source Domain Adaptation.
CoRR, 2022

Weight-variant Latent Causal Models.
CoRR, 2022

Causality-Based Multivariate Time Series Anomaly Detection.
CoRR, 2022

Offline Reinforcement Learning with Causal Structured World Models.
CoRR, 2022

Learning Latent Causal Dynamics.
CoRR, 2022

Counterfactual Fairness with Partially Known Causal Graph.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Identifiability of Nonlinear ICA: Sparsity and Beyond.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Temporally Disentangled Representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Unsupervised Image-to-Image Translation with Density Changing Regularization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Latent Hierarchical Causal Structure Discovery with Rank Constraints.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Factored Adaptation for Non-Stationary Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Truncated Matrix Power Iteration for Differentiable DAG Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identification of Linear Non-Gaussian Latent Hierarchical Structure.
Proceedings of the International Conference on Machine Learning, 2022

Partial disentanglement for domain adaptation.
Proceedings of the International Conference on Machine Learning, 2022

Action-Sufficient State Representation Learning for Control with Structural Constraints.
Proceedings of the International Conference on Machine Learning, 2022

Adversarial Robustness Through the Lens of Causality.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Temporally Causal Latent Processes from General Temporal Data.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Optimal Transport for Causal Discovery.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Conditional Contrastive Learning with Kernel.
Proceedings of the Tenth International Conference on Learning Representations, 2022

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Attainability and Optimality: The Equalized Odds Fairness Revisited.
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract).
Proceedings of the IEEE International Conference on Big Data, 2022

On the Convergence of Continuous Constrained Optimization for Structure Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Federated Bayesian Network Structure Learning with Continuous Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Invariant Action Effect Model for Reinforcement Learning.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Identification of Linear Latent Variable Model with Arbitrary Distribution.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Transfer Learning-Based Outdoor Position Recovery With Cellular Data.
IEEE Trans. Mob. Comput., 2021

Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models.
ACM Trans. Intell. Syst. Technol., 2021

Adversarial orthogonal regression: Two non-linear regressions for causal inference.
Neural Networks, 2021

Transferable Time-Series Forecasting under Causal Conditional Shift.
CoRR, 2021

A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy.
CoRR, 2021

Outdoor Position Recovery from HeterogeneousTelco Cellular Data.
CoRR, 2021

Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations.
CoRR, 2021

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders.
CoRR, 2021

Unpaired data empowers association tests.
Bioinform., 2021

Instance-dependent Label-noise Learning under a Structural Causal Model.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Progressive Open-Domain Response Generation with Multiple Controllable Attributes.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Unaligned Image-to-Image Translation by Learning to Reweight.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Testing Independence Between Linear Combinations for Causal Discovery.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Improving Causal Discovery By Optimal Bayesian Network Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Multiple player tracking in basketball court videos.
J. Real Time Image Process., 2020

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.
J. Mach. Learn. Res., 2020

Causal Discovery from Heterogeneous/Nonstationary Data.
J. Mach. Learn. Res., 2020

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation.
CoRR, 2020

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs.
CoRR, 2020

Attack on Multi-Node Attention for Object Detection.
CoRR, 2020

Learning from Positive and Unlabeled Data by Identifying the Annotation Process.
CoRR, 2020

Domain Adaptation As a Problem of Inference on Graphical Models.
CoRR, 2020

How do fair decisions fare in long-term qualification?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Causal View on Robustness of Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Domain Adaptation as a Problem of Inference on Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery.
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), 2020

Label-Noise Robust Domain Adaptation.
Proceedings of the 37th International Conference on Machine Learning, 2020

LTF: A Label Transformation Framework for Correcting Label Shift.
Proceedings of the 37th International Conference on Machine Learning, 2020

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs.
Proceedings of the 37th International Conference on Machine Learning, 2020

Adaptive Task Sampling for Meta-learning.
Proceedings of the Computer Vision - ECCV 2020, 2020

Generative-Discriminative Complementary Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Compressed Self-Attention for Deep Metric Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Introduction to the Special Section on Advances in Causal Discovery and Inference.
ACM Trans. Intell. Syst. Technol., 2019

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs.
CoRR, 2019

Transfer Learning-Based Outdoor Position Recovery with Telco Data.
CoRR, 2019

Disentanglement Challenge: From Regularization to Reconstruction.
CoRR, 2019

Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs.
CoRR, 2019

Identification of Effective Connectivity Subregions.
CoRR, 2019

Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach.
CoRR, 2019

Twin Auxiliary Classifiers GAN.
CoRR, 2019

Causal Discovery with Cascade Nonlinear Additive Noise Models.
CoRR, 2019

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.
CoRR, 2019

On Learning Invariant Representation for Domain Adaptation.
CoRR, 2019

Causal Discovery with General Non-Linear Relationships using Non-Linear ICA.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Domain Generalization via Multidomain Discriminant Analysis.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Twin Auxilary Classifiers GAN.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Likelihood-Free Overcomplete ICA and Applications In Causal Discovery.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Triad Constraints for Learning Causal Structure of Latent Variables.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery.
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019

Causal Discovery with Cascade Nonlinear Additive Noise Model.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Learning Disentangled Semantic Representation for Domain Adaptation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

On Learning Invariant Representations for Domain Adaptation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network.
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Causal Discovery in the Presence of Missing Data.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Data-Driven Approach to Multiple-Source Domain Adaptation.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Low-Dimensional Density Ratio Estimation for Covariate Shift Correction.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Guest Editorial: Special Issue on Causal Discovery 2017.
Int. J. Data Sci. Anal., 2018

Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping.
CoRR, 2018

Causal discovery in the presence of missing data.
CoRR, 2018

User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning.
CoRR, 2018

Causal Generative Domain Adaptation Networks.
CoRR, 2018

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Modeling Dynamic Missingness of Implicit Feedback for Recommendation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multi-domain Causal Structure Learning in Linear Systems.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Causal Discovery from Discrete Data using Hidden Compact Representation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery.
Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, 2018

Generalized Score Functions for Causal Discovery.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Deep Domain Generalization via Conditional Invariant Adversarial Networks.
Proceedings of the Computer Vision - ECCV 2018, 2018

Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Learning Vector Autoregressive Models With Latent Processes.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Guest editorial: special issue on causal discovery.
Int. J. Data Sci. Anal., 2017

Causality Refined Diagnostic Prediction.
CoRR, 2017

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions.
CoRR, 2017

A New Measure of Conditional Dependence for Causal Structural Learning.
CoRR, 2017

Causal Discovery from Temporally Aggregated Time Series.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Learning Causal Structures Using Regression Invariance.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Causal Learning and Machine Learning.
Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

Causal Discovery Using Regression-Based Conditional Independence Tests.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model.
ACM Trans. Intell. Syst. Technol., 2016

Preface to the ACM TIST Special Issue on Causal Discovery and Inference.
ACM Trans. Intell. Syst. Technol., 2016

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Learning Network of Multivariate Hawkes Processes: A Time Series Approach.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Domain Adaptation with Conditional Transferable Components.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Distinguishing Cause from Effect Based on Exogeneity.
CoRR, 2015

Towards Robust and Specific Causal Discovery from fMRI.
CoRR, 2015

Identification of Time-Dependent Causal Model: A Gaussian Process Treatment.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Discovering Temporal Causal Relations from Subsampled Data.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Multi-Source Domain Adaptation: A Causal View.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Causal Discovery via Reproducing Kernel Hilbert Space Embeddings.
Neural Comput., 2014

A Permutation-Based Kernel Conditional Independence Test.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

2013
Bridging Information Criteria and Parameter Shrinkage for Model Selection.
CoRR, 2013

Domain Adaptation under Target and Conditional Shift.
Proceedings of the 30th International Conference on Machine Learning, 2013

On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example.
Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013

Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Semi-supervised Learning in Causal and Anticausal Settings.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Information-geometric approach to inferring causal directions.
Artif. Intell., 2012

Causal discovery with scale-mixture model for spatiotemporal variance dependencies.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

On causal and anticausal learning.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
A General Linear Non-Gaussian State-Space Model.
Proceedings of the 3rd Asian Conference on Machine Learning, 2011

Robust Learning via Cause-Effect Models
CoRR, 2011

Testing whether linear equations are causal: A free probability theory approach.
Proceedings of the UAI 2011, 2011

Kernel-based Conditional Independence Test and Application in Causal Discovery.
Proceedings of the UAI 2011, 2011

2010
Nonlinear acyclic causal models.
Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity.
J. Mach. Learn. Res., 2010

Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion.
Neurocomputing, 2010

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery.
Proceedings of the UAI 2010, 2010

Source Separation and Higher-Order Causal Analysis of MEG and EEG.
Proceedings of the UAI 2010, 2010

Inferring deterministic causal relations.
Proceedings of the UAI 2010, 2010

Probabilistic latent variable models for distinguishing between cause and effect.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Multi-label learning by exploiting label dependency.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

2009
On the Identifiability of the Post-Nonlinear Causal Model.
Proceedings of the UAI 2009, 2009

Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

ICA with Sparse Connections: Revisited.
Proceedings of the Independent Component Analysis and Signal Separation, 2009

2007
Separating Convolutive Mixtures By Pairwise Mutual Information Minimization.
IEEE Signal Process. Lett., 2007

Independent Factor Reinforcement Learning for Portfolio Management.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2007

Nonlinear independent component analysis with minimal nonlinear distortion.
Proceedings of the Machine Learning, 2007

Kernel-Based Nonlinear Independent Component Analysis.
Proceedings of the Independent Component Analysis and Signal Separation, 2007

2006
Dimension reduction as a deflation method in ICA.
IEEE Signal Process. Lett., 2006

Symbol Recognition with Kernel Density Matching.
IEEE Trans. Pattern Anal. Mach. Intell., 2006

An Adaptive Method for Subband Decomposition ICA.
Neural Comput., 2006

ICA with Sparse Connections.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Extensions of ICA for Causality Discovery in the Hong Kong Stock Market.
Proceedings of the Neural Information Processing, 13th International Conference, 2006

Enhancement of Source Independence for Blind Source Separation.
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments.
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

2005
Extensions of independent component analysis: towards applications.
PhD thesis, 2005

Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures.
Neural Comput., 2005

To apply score function difference based ICA algorithms to high-dimensional data.
Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2003
Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA.
Proceedings of the Artificial Neural Networks and Neural Information Processing, 2003


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