Multi-Instance Learning with Distribution Change. - Weijia Zhang 001; Zhi-Hua Zhou (AAAI, 2014)

Mining heterogeneous causal effects for personalized cancer treatment. - Weijia Zhang 001; Thuc Duy Le; Lin Liu 003; Zhi-Hua Zhou; Jiuyong Li (Bioinform., 2017)

miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase. - Taosheng Xu; Ning Su; Lin Liu 003; Junpeng Zhang 001; Hongqiang Wang; Weijia Zhang 001; Jie Gui; Kui Yu; Jiuyong Li; Thuc Duy Le (BMC Bioinform., 2018)

Estimating heterogeneous treatment effect by balancing heterogeneity and fitness. - Weijia Zhang 001; Thuc Duy Le; Lin Liu 003; Jiuyong Li (BMC Bioinform., 2018)

Distributionally Robust Multi-instance Learning with Stable Instances. - Weijia Zhang 001; Jiuyong Li; Lin Liu 003 (CoRR, 2019)

Robust Multi-Instance Learning with Stable Instances. - Weijia Zhang 001; Lin Liu 003; Jiuyong Li (ECAI, 2020)

MONET: a toolbox integrating top-performing methods for network modularization. - Mattia Tomasoni; Sergio Gómez; Jake Crawford; Weijia Zhang 001; Sarvenaz Choobdar; Daniel Marbach; Sven Bergmann (Bioinform., 2020)

A unified survey on treatment effect heterogeneity modeling and uplift modeling. - Weijia Zhang 001; Jiuyong Li; Lin Liu 003 (CoRR, 2020)

Treatment Effect Estimation with Disentangled Latent Factors. - Weijia Zhang 001; Lin Liu 003; Jiuyong Li (AAAI, 2021)

Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder. - Weijia Zhang 001 (IJCAI, 2021)

A general framework for causal classification. - Jiuyong Li; Weijia Zhang 001; Lin Liu 003; Kui Yu; Thuc Duy Le; Jixue Liu (Int. J. Data Sci. Anal., 2021)

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization. - Weijia Zhang 001; Xuanhui Zhang; Hanwen Deng; Min-Ling Zhang (NeurIPS, 2022)

Towards Learning Causal Representations from Multi-Instance Bags. - Weijia Zhang 001; Xuanhui Zhang; Hanwen Deng; Min-Ling Zhang (CoRR, 2022)

A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling. - Weijia Zhang 001; Jiuyong Li; Lin Liu 003 (ACM Comput. Surv., 2022)

Imbalanced volunteer engagement in cultural heritage crowdsourcing: a task-related exploration based on causal inference. - Xuanhui Zhang; Weijia Zhang 001; Yuxiang (Chris) Zhao; Qinghua Zhu 002 (Inf. Process. Manag., 2022)

Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification. - Shujun Yang; Yu Zhang 006; Yuheng Jia; Weijia Zhang 001 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (NeurIPS, 2023)

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection. - Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2023)

Rethinking the Value of Labels for Instance-Dependent Label Noise Learning. - Hanwen Deng; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2023)

Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees. - Weijia Zhang 001; Chun Kai Ling; Xuanhui Zhang (AAAI, 2024)

Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection. - Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (ICME, 2024)

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (IJCAI, 2024)

Multi-Instance Partial-Label Learning with Margin Adjustment. - Wei Tang 017; Yin-Fang Yang; Zhaofei Wang; Weijia Zhang 001; Min-Ling Zhang (NeurIPS, 2024)

Multi-instance partial-label learning: towards exploiting dual inexact supervision. - Wei Tang 017; Weijia Zhang 001; Min-Ling Zhang (Sci. China Inf. Sci., 2024)

Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification. - Xin Liu 088; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2024)

HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks. - Xin Liu 088; Weijia Zhang 001; Min-Ling Zhang (CoRR, 2024)

Disentangled Representation Learning for Causal Inference with Instruments. - Debo Cheng; Jiuyong Li; Lin Liu 003; Ziqi Xu 001; Weijia Zhang 001; Jixue Liu; Thuc Duy Le (CoRR, 2024)

Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization. - Yizhi Wang; Weijia Zhang 001; Min-Ling Zhang (AAAI, 2025)

Instance-dependent label noise learning via separating style from content. - Han-Wen Deng; Weijia Zhang 001; Min-Ling Zhang (Pattern Recognit. Lett., 2025)