Kun Zhang

28

Papers

44

Total Citations

Papers (28)

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

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

Learning Counterfactual Outcomes Under Rank Preservation

Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness

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

Score-Based Causal Discovery of Latent Variable Causal Models

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

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