Clustering and projected clustering with adaptive neighbors. - Feiping Nie 001; Xiaoqian Wang 001; Heng Huang (KDD, 2014)

Discriminative Unsupervised Dimensionality Reduction. - Xiaoqian Wang 001; Yun Liu 021; Feiping Nie 001; Heng Huang (IJCAI, 2015)

The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. - Feiping Nie 001; Xiaoqian Wang 001; Michael I. Jordan; Heng Huang (AAAI, 2016)

New Robust Clustering Model for Identifying Cancer Genome Landscapes. - Hongchang Gao; Xiaoqian Wang 001; Heng Huang (ICDM, 2016)

Structured Doubly Stochastic Matrix for Graph Based Clustering: Structured Doubly Stochastic Matrix. - Xiaoqian Wang 001; Feiping Nie 001; Heng Huang (KDD, 2016)

Prediction of Memory Impairment with MRI Data: A Longitudinal Study of Alzheimer's Disease. - Xiaoqian Wang 001; Dinggang Shen; Heng Huang (MICCAI (1), 2016)

Multiclass Capped ℓp-Norm SVM for Robust Classifications. - Feiping Nie 001; Xiaoqian Wang 001; Heng Huang (AAAI, 2017)

Predicting Interrelated Alzheimer's Disease Outcomes via New Self-learned Structured Low-Rank Model. - Xiaoqian Wang 001; Kefei Liu 001; Jingwen Yan; Shannon L. Risacher; Andrew J. Saykin; Li Shen 001; Heng Huang (IPMI, 2017)

Group Sparse Additive Machine. - Hong Chen 004; Xiaoqian Wang 001; Cheng Deng 002; Heng Huang (NIPS, 2017)

Learning A Structured Optimal Bipartite Graph for Co-Clustering. - Feiping Nie 001; Xiaoqian Wang 001; Cheng Deng 002; Heng Huang (NIPS, 2017)

Regularized Modal Regression with Applications in Cognitive Impairment Prediction. - Xiaoqian Wang 001; Hong Chen 004; Weidong Cai 001; Dinggang Shen; Heng Huang (NIPS, 2017)

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model. - Xiaoqian Wang 001; Jingwen Yan; Xiaohui Yao; Sungeun Kim; Kwangsik Nho; Shannon L. Risacher; Andrew J. Saykin; Li Shen 001; Heng Huang (RECOMB, 2017)

Directional Label Rectification in Adaptive Graph. - Xiaoqian Wang 001; Hao Huang (AAAI, 2018)

New Balanced Active Learning Model and Optimization Algorithm. - Xiaoqian Wang 001; Yijun Huang; Ji Liu; Heng Huang (IJCAI, 2018)

Semi-Supervised Generative Adversarial Network for Gene Expression Inference. - Kamran Ghasedi Dizaji; Xiaoqian Wang 001; Heng Huang (KDD, 2018)

Temporal Correlation Structure Learning for MCI Conversion Prediction. - Xiaoqian Wang 001; Weidong Cai 001; Dinggang Shen; Heng Huang (MICCAI (3), 2018)

Quantitative trait loci identification for brain endophenotypes via new additive model with random networks. - Xiaoqian Wang 001; Hong Chen 004; Jingwen Yan; Kwangsik Nho; Shannon L. Risacher; Andrew J. Saykin; Li Shen 001; Heng Huang (Bioinform., 2018)

Conditional generative adversarial network for gene expression inference. - Xiaoqian Wang 001; Kamran Ghasedi Dizaji; Heng Huang (Bioinform., 2018)

Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model. - Xiaoqian Wang 001; Jingwen Yan; Xiaohui Yao; Sungeun Kim; Kwangsik Nho; Shannon L. Risacher; Andrew J. Saykin; Li Shen 001; Heng Huang (J. Comput. Biol., 2018)

Cognitive Assessment Prediction in Alzheimer's Disease by Multi-Layer Multi-Target Regression. - Xiaoqian Wang 001; Xiantong Zhen; Quanzheng Li; Dinggang Shen; Heng Huang (Neuroinformatics, 2018)

Balanced Self-Paced Learning for Generative Adversarial Clustering Network. - Kamran Ghasedi; Xiaoqian Wang 001; Cheng Deng 002; Heng Huang (CVPR, 2019)

An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms. - Feiping Nie 001; Zhanxuan Hu; Xiaoqian Wang 001; Rong Wang 001; Xuelong Li 001; Heng Huang (CoRR, 2019)

Approaching Machine Learning Fairness through Adversarial Network. - Xiaoqian Wang 001; Heng Huang (CoRR, 2019)

Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks. - Yawen Huang; Feng Zheng; Danyang Wang; Junyu Jiang; Xiaoqian Wang 001; Ling Shao 001 (IJCAI, 2020)

Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting. - Haoyan Xu; Ziheng Duan; Yida Huang; Jie Feng 006; Anni Ren; Qianru Zhang; Pengyu Song; Xiaoqian Wang 001 (CoRR, 2020)

Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks. - Yida Huang; Haoyan Xu; Ziheng Duan; Anni Ren; Jie Feng 006; Qianru Zhang; Xiaoqian Wang 001 (CoRR, 2020)

Multivariate Time Series Classification with Hierarchical Variational Graph Pooling. - Haoyan Xu; Ziheng Duan; Yunsheng Bai; Yida Huang; Anni Ren; Qianru Yu; Qianru Zhang; Yueyang Wang; Xiaoqian Wang 001; Yizhou Sun; Wei Wang 010 (CoRR, 2020)

Constructing a Fair Classifier with Generated Fair Data. - Taeuk Jang; Feng Zheng; Xiaoqian Wang 001 (AAAI, 2021)

Multi-Task Learning Via Sharing Inexact Low-Rank Subspace. - Xiaoqian Wang 001; Feiping Nie 001 (ICASSP, 2021)

Shapley Explanation Networks. - Rui Wang; Xiaoqian Wang 001; David I. Inouye (ICLR, 2021)

On the Convergence of Stochastic Compositional Gradient Descent Ascent Method. - Hongchang Gao; Xiaoqian Wang 001; Lei Luo; Xinghua Shi (IJCAI, 2021)

Self-Interpretable Model with Transformation Equivariant Interpretation. - Yipei Wang; Xiaoqian Wang 001 (NeurIPS, 2021)

Self-Interpretable Model with TransformationEquivariant Interpretation. - Yipei Wang; Xiaoqian Wang 001 (CoRR, 2021)

Group-Aware Threshold Adaptation for Fair Classification. - Taeuk Jang; Pengyi Shi; Xiaoqian Wang 001 (AAAI, 2022)

Fairness with Adaptive Weights. - Junyi Chai 004; Xiaoqian Wang 001 (ICML, 2022)

Fairness without Demographics through Knowledge Distillation. - Junyi Chai 004; Taeuk Jang; Xiaoqian Wang 001 (NeurIPS, 2022)

Self-Supervised Fair Representation Learning without Demographics. - Junyi Chai 004; Xiaoqian Wang 001 (NeurIPS, 2022)

"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations. - Yipei Wang; Xiaoqian Wang 001 (NeurIPS, 2022)

A Unified Study of Machine Learning Explanation Evaluation Metrics. - Yipei Wang; Xiaoqian Wang 001 (CoRR, 2022)

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification. - Tianci Liu 003; Haoyu Wang 004; Yaqing Wang 001; Xiaoqian Wang 001; Lu Su 001; Jing Gao 004 (AAAI, 2023)

Difficulty-Based Sampling for Debiased Contrastive Representation Learning. - Taeuk Jang; Xiaoqian Wang 001 (CVPR, 2023)

To be Robust and to be Fair: Aligning Fairness with Robustness. - Junyi Chai 004; Xiaoqian Wang 001 (CoRR, 2023)

Towards Trustworthy Artificial Intelligence for Equitable Global Health. - Hong Qin 003; Jude Kong; Wandi Ding; Ramneek Ahluwalia; Christo El-Morr; Zeynep Engin; Jake Okechukwu Effoduh; Rebecca Hwa; Serena Jingchuan Guo; Laleh Seyyed-Kalantari; Sylvia Kiwuwa Muyingo; Candace Makeda Moore; et al. (CoRR, 2023)

Iteratively Re-Weighted Method for Sparsity-Inducing Norms. - Feiping Nie 001; Zhanxuan Hu; Xiaoqian Wang 001; Xuelong Li 001; Heng Huang (IEEE Trans. Knowl. Data Eng., 2023)

Adversarial Fairness Network. - Taeuk Jang; Xiaoqian Wang 001; Heng Huang (AAAI, 2024)

Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow. - Tianchun Li; Chengxiang Wu; Pengyi Shi; Xiaoqian Wang 001 (AAAI, 2024)

Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. - Taeuk Jang; Hongchang Gao; Pengyi Shi; Xiaoqian Wang 001 (AISTATS, 2024)

On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective. - Yipei Wang; Xiaoqian Wang 001 (AISTATS, 2024)

Inverse Problem Antidote (IPA): Modeling of Systems Biology Model with Invertible Neural Networks. - Linlin Li; Shenyu Lu; David M. Umulis; Xiaoqian Wang 001 (BIBM, 2024)

FADES: Fair Disentanglement with Sensitive Relevance. - Taeuk Jang; Xiaoqian Wang 001 (CVPR, 2024)

Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch. - Xidong Wu; Shangqian Gao; Zeyu Zhang; Zhenzhen Li; Runxue Bao; Yanfu Zhang; Xiaoqian Wang 001; Heng Huang (CVPR, 2024)

Fairness-Aware Online Positive-Unlabeled Learning. - Hoin Jung; Xiaoqian Wang 001 (EMNLP (Industry Track), 2024)

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation. - Xiaoze Liu; Ting Sun 004; Tianyang Xu; Feijie Wu; Cunxiang Wang; Xiaoqian Wang 001; Jing Gao 004 (EMNLP, 2024)

Debiasing Attention Mechanism in Transformer without Demographics. - Shenyu Lu; Yipei Wang; Xiaoqian Wang 001 (ICLR, 2024)

Benchmarking Deletion Metrics with the Principled Explanations. - Yipei Wang; Xiaoqian Wang 001 (ICML, 2024)

Learning the Irreversible Progression Trajectory of Alzheimer's Disease. - Yipei Wang; Bing He; Shannon L. Risacher; Andrew J. Saykin; Jingwen Yan; Xiaoqian Wang 001 (ISBI, 2024)

Neural Collapse Inspired Debiased Representation Learning for Min-max Fairness. - Shenyu Lu; Junyi Chai 004; Xiaoqian Wang 001 (KDD, 2024)

A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks. - Hoin Jung; Taeuk Jang; Xiaoqian Wang 001 (NeurIPS, 2024)

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs. - Xiaoze Liu; Feijie Wu; Tianyang Xu; Zhuo Chen 007; Yichi Zhang 009; Xiaoqian Wang 001; Jing Gao 004 (IEEE Data Eng. Bull., 2024)

Identifying and Mitigating Spurious Correlation in Multi-Task Learning. - Junyi Chai 004; Shenyu Lu; Xiaoqian Wang 001 (CVPR, 2025)

Adversarial Latent Feature Augmentation for Fairness. - Hoin Jung; Junyi Chai 004; Xiaoqian Wang 001 (ICLR, 2025)

Mitigating Spurious Correlations in Zero-Shot Multimodal Models. - Shenyu Lu; Junyi Chai 004; Xiaoqian Wang 001 (ICLR, 2025)

Towards On-the-Fly Novel Category Discovery in Dynamic Long-Tailed Distributions. - Hoin Jung; Xiaoqian Wang 001 (WACV, 2025)

SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning. - Tianyang Xu; Xiaoze Liu; Feijie Wu; Xiaoqian Wang 001; Jing Gao 004 (CoRR, 2025)