Weijia Zhang

Pattern Recognit. Lett., 2025

Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization.

[BibT_eX]

[DOI]

Yizhi Wang

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Disentangled Representation Learning for Causal Inference with Instruments.

[BibT_eX]

[DOI]

CoRR, 2024

HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks.

[BibT_eX]

[DOI]

Xin Liu

CoRR, 2024

Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification.

[BibT_eX]

[DOI]

Xin Liu

CoRR, 2024

Multi-instance partial-label learning: towards exploiting dual inexact supervision.

[BibT_eX]

[DOI]

Wei Tang

Sci. China Inf. Sci., 2024

Multi-Instance Partial-Label Learning with Margin Adjustment.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning.

[BibT_eX]

[DOI]

Wei Tang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection.

[BibT_eX]

[DOI]

Zhaofei Wang

Proceedings of the IEEE International Conference on Multimedia and Expo, 2024

Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees.

[BibT_eX]

[DOI]

Chun Kai Ling

Xuanhui Zhang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Rethinking the Value of Labels for Instance-Dependent Label Noise Learning.

[BibT_eX]

[DOI]

Hanwen Deng

CoRR, 2023

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection.

[BibT_eX]

[DOI]

Zhaofei Wang

CoRR, 2023

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning.

[BibT_eX]

[DOI]

Wei Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Imbalanced volunteer engagement in cultural heritage crowdsourcing: a task-related exploration based on causal inference.

[BibT_eX]

[DOI]

Inf. Process. Manag., 2022

A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling.

[BibT_eX]

[DOI]

ACM Comput. Surv., 2022

Towards Learning Causal Representations from Multi-Instance Bags.

[BibT_eX]

[DOI]

CoRR, 2022

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

A general framework for causal classification.

[BibT_eX]

[DOI]

Int. J. Data Sci. Anal., 2021

Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Treatment Effect Estimation with Disentangled Latent Factors.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

A unified survey on treatment effect heterogeneity modeling and uplift modeling.

[BibT_eX]

[DOI]

CoRR, 2020

MONET: a toolbox integrating top-performing methods for network modularization.

[BibT_eX]

[DOI]

Bioinform., 2020

Robust Multi-Instance Learning with Stable Instances.

[BibT_eX]

[DOI]

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019

Distributionally Robust Multi-instance Learning with Stable Instances.

[BibT_eX]

[DOI]