Kun Zhang

80

Papers

112

Total Citations

Papers (80)

Learning Causal Structures Using Regression Invariance

NeurIPS 2017arXiv

Federated Causal Discovery from Heterogeneous Data

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability

Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions

SmartCLIP: Modular Vision-language Alignment with Identification Guarantees

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders

Detecting Generated Images by Fitting Natural Image Distributions

Identification of Intermittent Temporal Latent Process

Dynamic Expansion Diffusion Learning for Lifelong Generative Modelling

Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data

The third pillar of causal analysis? A measurement perspective on causal representations

Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness

Learning Counterfactual Outcomes Under Rank Preservation

DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning

Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint

Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation

Negative-Aware Attention Framework for Image-Text Matching

Understanding Masked Autoencoders via Hierarchical Latent Variable Models

Unpaired Image-to-Image Translation With Shortest Path Regularization

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

SmartBrush: Text and Shape Guided Object Inpainting With Diffusion Model

DAE-GAN: Dynamic Aspect-Aware GAN for Text-to-Image Synthesis

Unaligned Image-to-Image Translation by Learning to Reweight

Tem-Adapter: Adapting Image-Text Pretraining for Video Question Answer

OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad

Hierarchy-Aware Pseudo Word Learning with Text Adaptation for Zero-Shot Composed Image Retrieval

Continual Unsupervised Generative Modelling via Online Optimal Transport

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

Identification of Necessary Semantic Undertakers in the Causal View for Image-Text Matching

Tree-of-Reasoning Question Decomposition for Complex Question Answering with Large Language Models

ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

Optimal Kernel Choice for Score Function-based Causal Discovery

Empowering Graph Invariance Learning with Deep Spurious Infomax

Causal Representation Learning from Multiple Distributions: A General Setting

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data

Detecting and Identifying Selection Structure in Sequential Data

Score-Based Causal Discovery of Latent Variable Causal Models

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

On Learning Invariant Representations for Domain Adaptation

Multi-domain Causal Structure Learning in Linear Systems

Causal Discovery from Discrete Data using Hidden Compact Representation

Modeling Dynamic Missingness of Implicit Feedback for Recommendation

Likelihood-Free Overcomplete ICA and Applications In Causal Discovery

NeurIPS 2019arXiv

Twin Auxilary Classifiers GAN

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Triad Constraints for Learning Causal Structure of Latent Variables

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

A Causal View on Robustness of Neural Networks

Domain Adaptation as a Problem of Inference on Graphical Models

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

How do fair decisions fare in long-term qualification?

Instance-dependent Label-noise Learning under a Structural Causal Model

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

Counterfactual Fairness with Partially Known Causal Graph

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Latent Hierarchical Causal Structure Discovery with Rank Constraints

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

Truncated Matrix Power Iteration for Differentiable DAG Learning

Temporally Disentangled Representation Learning

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

Unsupervised Image-to-Image Translation with Density Changing Regularization

Factored Adaptation for Non-Stationary Reinforcement Learning

Identification of Nonlinear Latent Hierarchical Models

Temporally Disentangled Representation Learning under Unknown Nonstationarity

Generalizing Nonlinear ICA Beyond Structural Sparsity

Disentangling Cognitive Diagnosis with Limited Exercise Labels

Learning World Models with Identifiable Factorization

Subspace Identification for Multi-Source Domain Adaptation

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity

Counterfactual Generation with Identifiability Guarantees

Discovering Temporal Causal Relations from Subsampled Data

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Domain Adaptation with Conditional Transferable Components

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models