Are Anchor Points Really Indispensable in Label-Noise Learning? - Xiaobo Xia; Tongliang Liu; Nannan Wang 001; Bo Han 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (NeurIPS, 2020)

Multi-Class Classification from Noisy-Similarity-Labeled Data. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Class2Simi: A New Perspective on Learning with Label Noise. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Parts-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (CoRR, 2020)

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Jiankang Deng; Jiatong Li; Yinian Mao (CoRR, 2020)

Robust early-learning: Hindering the memorization of noisy labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Chen Gong 002; Nannan Wang 001; Zongyuan Ge; Yi Chang (ICLR, 2021)

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (ICML, 2021)

BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells. - Fei Long; Jing-Jie Peng; Weitao Song; Xiaobo Xia; Jun Sang (Comput. Methods Programs Biomed., 2021)

Instance Correction for Learning with Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

Kernel Mean Estimation by Marginalized Corrupted Distributions. - Xiaobo Xia; Shuo Shan; Mingming Gong; Nannan Wang 001; Fei Gao 006; Haikun Wei; Tongliang Liu (CoRR, 2021)

Learning lightweight super-resolution networks with weight pruning. - Xinrui Jiang; Nannan Wang 001; Jingwei Xin; Xiaobo Xia; Xi Yang 011; Xinbo Gao 001 (Neural Networks, 2021)

Selective-Supervised Contrastive Learning with Noisy Labels. - Shikun Li; Xiaobo Xia; Shiming Ge; Tongliang Liu (CVPR, 2022)

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (ICLR, 2022)

Objects in Semantic Topology. - Shuo Yang 006; Peize Sun; Yi Jiang 004; Xiaobo Xia; Ruiheng Zhang 001; Zehuan Yuan; Changhu Wang; Ping Luo 002; Min Xu 001 (ICLR, 2022)

Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. - Xiaobo Xia; Shuo Shan; Mingming Gong; Nannan Wang 001; Fei Gao 006; Haikun Wei; Tongliang Liu (KDD, 2022)

Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. - Yewen Li; Chaojie Wang 001; Xiaobo Xia; Tongliang Liu; Xin Miao; Bo An 001 (NeurIPS, 2022)

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. - Shikun Li; Xiaobo Xia; Hansong Zhang 003; Yibing Zhan; Shiming Ge; Tongliang Liu (NeurIPS, 2022)

Pluralistic Image Completion with Gaussian Mixture Models. - Xiaobo Xia; Wenhao Yang; Jie Ren; Yewen Li; Yibing Zhan; Bo Han 003; Tongliang Liu (NeurIPS, 2022)

Pluralistic Image Completion with Probabilistic Mixture-of-Experts. - Xiaobo Xia; Wenhao Yang; Jie Ren; Yewen Li; Yibing Zhan; Bo Han 003; Tongliang Liu (CoRR, 2022)

LR-SVM+: Learning Using Privileged Information with Noisy Labels. - Zhengning Wu; Xiaobo Xia; Ruxin Wang 002; Jiatong Li; Jun Yu 001; Yinian Mao; Tongliang Liu (IEEE Trans. Multim., 2022)

A machine learning approach for predicting human shortest path task performance. - Shijun Cai; Seok-Hee Hong 001; Xiaobo Xia; Tongliang Liu; Weidong Huang 001 (Vis. Informatics, 2022)

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. - Zhuo Huang; Miaoxi Zhu; Xiaobo Xia; Li Shen 008; Jun Yu 001; Chen Gong 002; Bo Han 003; Bo Du 001; Tongliang Liu (CVPR, 2023)

HumanMAC: Masked Motion Completion for Human Motion Prediction. - Ling-Hao Chen; Jiawei Zhang; Yewen Li; Yiren Pang; Xiaobo Xia; Tongliang Liu (ICCV, 2023)

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. - Xiaobo Xia; Bo Han 003; Yibing Zhan; Jun Yu 001; Mingming Gong; Chen Gong 002; Tongliang Liu (ICCV, 2023)

Holistic Label Correction for Noisy Multi-Label Classification. - Xiaobo Xia; Jiankang Deng; Wei Bao; Yuxuan Du; Bo Han 003; Shiguang Shan; Tongliang Liu (ICCV, 2023)

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. - Zhuo Huang; Xiaobo Xia; Li Shen 008; Bo Han 003; Mingming Gong; Chen Gong 002; Tongliang Liu (ICLR, 2023)

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. - Yong Lin; Renjie Pi; Weizhong Zhang; Xiaobo Xia; Jiahui Gao; Xiao Zhou; Tongliang Liu; Bo Han 003 (ICLR, 2023)

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. - Xiaobo Xia; Jiale Liu; Jun Yu 001; Xu Shen 001; Bo Han 003; Tongliang Liu (ICLR, 2023)

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. - Haotian Zheng; Qizhou Wang; Zhen Fang 001; Xiaobo Xia; Feng Liu 003; Tongliang Liu; Bo Han 003 (NeurIPS, 2023)

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. - Yuhao Wu; Xiaobo Xia; Jun Yu 001; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Tongliang Liu (CoRR, 2023)

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm. - Shikun Li; Xiaobo Xia; Hansong Zhang 003; Shiming Ge; Tongliang Liu (CoRR, 2023)

VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence. - Jianing Qiu; Jian Wu; Hao Wei; Peilun Shi; Minqing Zhang; Yunyun Sun; Lin Li 070; Hanruo Liu; Hongyi Liu; Simeng Hou; Yuyang Zhao; Xuehui Shi; et al. (CoRR, 2023)

Coreset Selection with Prioritized Multiple Objectives. - Xiaobo Xia; Jiale Liu; Shaokun Zhang; Qingyun Wu; Tongliang Liu (CoRR, 2023)

One Shot Learning as Instruction Data Prospector for Large Language Models. - Yunshui Li; Binyuan Hui; Xiaobo Xia; Jiaxi Yang; Min Yang 007; Lei Zhang 201; Shuzheng Si; Junhao Liu 001; Tongliang Liu; Fei Huang 002; Yongbin Li (CoRR, 2023)

Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. - Xiaobo Xia; Bo Han 003; Nannan Wang 001; Jiankang Deng; Jiatong Li; Yinian Mao; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Dynamics-aware loss for learning with label noise. - Xiu-Chuan Li; Xiaobo Xia; Fei Zhu 004; Tongliang Liu; Xu-Yao Zhang; Cheng-Lin Liu 001 (Pattern Recognit., 2023)

One-Shot Learning as Instruction Data Prospector for Large Language Models. - Yunshui Li; Binyuan Hui; Xiaobo Xia; Jiaxi Yang; Min Yang 007; Lei Zhang 201; Shuzheng Si; Ling-Hao Chen; Junhao Liu 001; Tongliang Liu; Fei Huang 002; Yongbin Li (ACL (1), 2024)

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. - Ling-Hao Chen; Yuanshuo Zhang; Taohua Huang; Liangcai Su; Zeyi Lin; Xi Xiao; Xiaobo Xia; Tongliang Liu (CIKM, 2024)

IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. - Shaokun Zhang; Xiaobo Xia; Zhaoqing Wang; Ling-Hao Chen; Jiale Liu; Qingyun Wu; Tongliang Liu (ICLR, 2024)

Towards Realistic Model Selection for Semi-supervised Learning. - Muyang Li; Xiaobo Xia; Runze Wu; Fengming Huang; Jun Yu 001; Bo Han 003; Tongliang Liu (ICML, 2024)

Mitigating Label Noise on Graphs via Topological Sample Selection. - Yuhao Wu; Jiangchao Yao; Xiaobo Xia; Jun Yu 001; Ruxin Wang 002; Bo Han 003; Tongliang Liu (ICML, 2024)

Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints. - Xiaobo Xia; Jiale Liu; Shaokun Zhang; Qingyun Wu; Hongxin Wei; Tongliang Liu (ICML, 2024)

Few-Shot Adversarial Prompt Learning on Vision-Language Models. - Yiwei Zhou; Xiaobo Xia; Zhiwei Lin; Bo Han 003; Tongliang Liu (NeurIPS, 2024)

Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision. - Zhaoqing Wang; Xiaobo Xia; Ziye Chen; Xiao He; Yandong Guo; Mingming Gong; Tongliang Liu (CoRR, 2024)

Mitigating Label Noise on Graph via Topological Sample Selection. - Yuhao Wu; Jiangchao Yao; Xiaobo Xia; Jun Yu 001; Ruxin Wang 002; Bo Han 003; Tongliang Liu (CoRR, 2024)

DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception. - Run Luo; Yunshui Li; Longze Chen; Wanwei He; Ting-En Lin; Ziqiang Liu; Lei Zhang 201; Zikai Song; Xiaobo Xia; Tongliang Liu; Min Yang 007; Binyuan Hui (CoRR, 2024)

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs. - Lei Zhang 201; Yunshui Li; Jiaming Li 004; Xiaobo Xia; Jiaxi Yang; Run Luo; Minzheng Wang 001; Longze Chen; Junhao Liu 001; Min Yang 007 (CoRR, 2024)

Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection. - Yewen Li; Chaojie Wang 001; Xiaobo Xia; Xu He; Ruyi An; Dong Li 016; Tongliang Liu; Bo An 001; Xinrun Wang (CoRR, 2024)

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct. - Run Luo; Haonan Zhang; Longze Chen; Ting-En Lin; Xiong Liu; Yuchuan Wu; Min Yang 007; Minzheng Wang 001; Pengpeng Zeng; Lianli Gao; Heng Tao Shen; Yunshui Li; et al. (CoRR, 2024)

Towards Modality Generalization: A Benchmark and Prospective Analysis. - Xiaohao Liu; Xiaobo Xia; Zhuo Huang; Tat-Seng Chua (CoRR, 2024)

Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification. - Zhengning Wu; Tianyu He; Xiaobo Xia; Jun Yu 001; Xu Shen 001; Tongliang Liu (IEEE Trans. Multim., 2024)

Regularly Truncated M-Estimators for Learning With Noisy Labels. - Xiaobo Xia; Pengqian Lu; Chen Gong 002; Bo Han 003; Jun Yu 001; Jun Yu 002; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Tackling Noisy Labels With Network Parameter Additive Decomposition. - Jingyi Wang; Xiaobo Xia; Long Lan; Xinghao Wu; Jun Yu 001; Wenjing Yang 002; Bo Han 003; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Transferring Annotator- and Instance-Dependent Transition Matrix for Learning From Crowds. - Shikun Li; Xiaobo Xia; Jiankang Deng; Shiming Ge; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs. - Lei Zhang 201; Yunshui Li; Jiaming Li 004; Xiaobo Xia; Jiaxi Yang; Run Luo; Minzheng Wang 001; Longze Chen; Junhao Liu 001; Qiang Qu; Min Yang 007 (AAAI, 2025)

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct. - Run Luo; Haonan Zhang; Longze Chen; Ting-En Lin; Xiong Liu; Yuchuan Wu; Min Yang 007; Yongbin Li; Minzheng Wang 001; Pengpeng Zeng; Lianli Gao; Heng Tao Shen; et al. (ACL (Findings), 2025)

LaVin-DiT: Large Vision Diffusion Transformer. - Zhaoqing Wang; Xiaobo Xia; Runnan Chen; Dongdong Yu; Changhu Wang; Mingming Gong; Tongliang Liu (CVPR, 2025)

DEEM: Diffusion models serve as the eyes of large language models for image perception. - Run Luo; Yunshui Li; Longze Chen; Wanwei He; Ting-En Lin; Ziqiang Liu; Lei Zhang 201; Zikai Song; Hamid Rokny; Xiaobo Xia; Tongliang Liu; Binyuan Hui; et al. (ICLR, 2025)

A three-tier AI solution for equitable glaucoma diagnosis across China's hierarchical healthcare system. - Yi Zhou; Haitao Nie; Xinyu Gong; Minhui Dai; Zhaohong Guo; Xiaoling Deng; Mengyang Li; Yong Liu; Lingyu Sun; Xiangyi Tang; Ling Zhou; Zhiyao Tang; et al. (npj Digit. Medicine, 2025)

DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization. - Zhenglin Zhou; Xiaobo Xia; Fan Ma; Hehe Fan; Yi Yang 001; Tat-Seng Chua (CoRR, 2025)

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction. - Xiaobo Xia; Xiaofeng Liu; Jiale Liu; Kuai Fang; Lu Lu; Samet Oymak; William S. Currie; Tongliang Liu (CoRR, 2025)

Continual Multimodal Contrastive Learning. - Xiaohao Liu; Xiaobo Xia; See-Kiong Ng; Tat-Seng Chua (CoRR, 2025)

GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents. - Run Luo; Lu Wang; Wanwei He; Xiaobo Xia (CoRR, 2025)

VCM: Vision Concept Modeling Based on Implicit Contrastive Learning with Vision-Language Instruction Fine-Tuning. - Run Luo; Renke Shan; Longze Chen; Ziqiang Liu; Lu Wang; Min Yang 007; Xiaobo Xia (CoRR, 2025)

L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models. - Xiaohao Liu; Xiaobo Xia; Weixiang Zhao; Manyi Zhang; Xianzhi Yu; Xiu Su; Shuo Yang; See-Kiong Ng; Tat-Seng Chua (CoRR, 2025)

Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score. - Xuanning Zhou; Hao Zeng 005; Xiaobo Xia; Bingyi Jing; Hongxin Wei (CoRR, 2025)

LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment. - Shikun Li; Shipeng Li; Zhiqin Yang; Xinghua Zhang 001; Gaode Chen; Xiaobo Xia; Hengyu Liu; Zhe Peng (CoRR, 2025)

Where, What, Why: Towards Explainable Driver Attention Prediction. - Yuchen Zhou 002; Jiayu Tang; Xiaoyan Xiao; Yueyao Lin; Linkai Liu; Zipeng Guo; Hao Fei 001; Xiaobo Xia; Chao Gou (CoRR, 2025)

Principled Multimodal Representation Learning. - Xiaohao Liu; Xiaobo Xia; See-Kiong Ng; Tat-Seng Chua (CoRR, 2025)

Logic Unseen: Revealing the Logical Blindspots of Vision-Language Models. - Yuchen Zhou 002; Jiayu Tang; Shuo Yang; Xiaoyan Xiao; Yuqin Dai; Wenhao Yang; Chao Gou; Xiaobo Xia; Tat-Seng Chua (CoRR, 2025)