Tongliang Liu

发表

Domain Adaptation with Conditional Transferable Components

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2016, ICML.

Deep Domain Generalization via Conditional Invariant Adversarial Networks

D. Tao, Mingming Gong, Kun Zhang, 2018, ECCV.

On the robustness and generalization of Cauchy regression

D. Tao, Tongliang Liu, 2014, 2014 4th IEEE International Conference on Information Science and Technology.

Classification with Noisy Labels by Importance Reweighting

Dacheng Tao, Tongliang Liu, D. Tao, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Domain Generalization via Conditional Invariant Representation pdf

Dacheng Tao, Ya Li, Mingming Gong, 2018, ArXiv.

Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning

D. Tao, Tongliang Liu, Naiyang Guan, 2017 .

A machine learning approach for predicting human shortest path task performance

Seok-Hee Hong, Tongliang Liu, Shijun Cai, 2022, Visual Informatics.

LTF: A Label Transformation Framework for Correcting Target Shift

D. Tao, Mingming Gong, Tongliang Liu, 2020 .

The complexity of algorithmic hypothesis class

Tongliang Liu, 2016 .

Confident Anchor-Induced Multi-Source Free Domain Adaptation

Tongliang Liu, Jiahua Dong, Zhen Fang, 2021, NeurIPS.

Not “ monkey ” Not “ prairie dog ” Not “ meerkat ” ComplementaryLabel True Label Meerkat MonkeyPrairie Dog

D. Tao, Mingming Gong, Tongliang Liu, 2018 .

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Dacheng Tao, Mingli Song, Tongliang Liu, 2017, IEEE transactions on pattern analysis and machine intelligence.

1 Generalization in Classical Statistical Learning Theory

D. Tao, Tongliang Liu, Jingwei Zhang, 2018 .

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Tongliang Liu, Jia Wu, Dacheng Tao, 2022, ArXiv.

Maximum Mean Discrepancy is Aware of Adversarial Attacks pdf

Tongliang Liu, Masashi Sugiyama, Bo Han, 2020, ArXiv.

Understanding and Improving Early Stopping for Learning with Noisy Labels Supplementary

Tongliang Liu, Bo Han, Yinian Mao, 2021 .

Recent Advances for Quantum Neural Networks in Generative Learning

Tongliang Liu, Min-Hsiu Hsieh, Kaining Zhang, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Mingming Gong, Kun Zhang, Tongliang Liu, 2022, 2205.13869.

Quantum Differentially Private Sparse Regression Learning

D. Tao, Tongliang Liu, Min-Hsiu Hsieh, 2020, IEEE Transactions on Information Theory.

Invariance Principle Meets Out-of-Distribution Generalization on Graphs

Tongliang Liu, James Cheng, Han Yang, 2022, arXiv.org.

Instance-dependent Label-noise Learning under a Structural Causal Model

Mingming Gong, Tongliang Liu, Bo Han, 2021, NeurIPS.

CausalAdv: Adversarial Robustness through the Lens of Causality

B. Schölkopf, B. Scholkopf, Mingming Gong, 2021, 2106.06196.

Expressive power of parametrized quantum circuits

Tongliang Liu, Min-Hsiu Hsieh, Dacheng Tao, 2020 .

Domain Generalization via Entropy Regularization

Tongliang Liu, Dacheng Tao, Mingming Gong, 2020, NeurIPS.

MSR: Making Self-supervised learning Robust to Aggressive Augmentations

Tongliang Liu, Bo Han, Erkun Yang, 2022, ArXiv.

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

Xiansheng Hua, Tongliang Liu, Xinchao Wang, 2021, ArXiv.

Pluralistic Image Completion with Probabilistic Mixture-of-Experts

Tongliang Liu, Bo Han, Xiaobo Xia, 2022, ArXiv.

Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos

Tongliang Liu, Lianyang Ma, Yu Yao, 2022, ArXiv.

Modeling Adversarial Noise for Adversarial Training

Tongliang Liu, Bo Han, Dawei Zhou, 2021, ICML.

Improving Adversarial Robustness via Mutual Information Estimation

Tongliang Liu, Bo Han, Yibing Zhan, 2022, ICML.

Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data

Mingming Gong, Tongliang Liu, Haikun Wei, 2022, Knowledge Discovery and Data Mining.

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning

Masashi Sugiyama, M. Sugiyama, D. Tao, 2020, ICLR.

Counterfactual Fairness with Partially Known Causal Graph

Mingming Gong, Kun Zhang, Tongliang Liu, 2022, ArXiv.

Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos

Tongliang Liu, Lianyang Ma, Yu Yao, 2022, ArXiv.

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

Masashi Sugiyama, M. Sugiyama, Tongliang Liu, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Mutual Quantization for Cross-Modal Search with Noisy Labels

Tongliang Liu, Erkun Yang, Cheng Deng, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

U NDERSTANDING AND I MPROVING G RAPH I NJECTION A TTACK BY P ROMOTING U NNOTICEABILITY

Tongliang Liu, Han Yang, Yonggang Zhang, 2022 .

C AUSAL A DV : A DVERSARIAL R OBUSTNESS THROUGH THE L ENS OF C AUSALITY

B. Schölkopf, Mingming Gong, Tongliang Liu, 2022 .

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

Tongliang Liu, Bo Han, Gang Niu, 2021, ICLR.

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

J. Kwok, Tongliang Liu, Bo Han, 2021, NeurIPS.

A Grover-search based quantum learning scheme for classification

D. Tao, Tongliang Liu, Min-Hsiu Hsieh, 2018, New Journal of Physics.

Symmetric Pruning in Quantum Neural Networks

Tongliang Liu, Dacheng Tao, Yuxuan Du, 2022, 2208.14057.

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Yatao Bian, Tongliang Liu, Han Yang, 2022, NeurIPS.

Diversified Bayesian Nonnegative Matrix Factorization

Dacheng Tao, Tongliang Liu, Maoying Qiao, 2020, AAAI.

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

Mingming Gong, Tongliang Liu, Dacheng Tao, 2022, ArXiv.

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Mingming Gong, Tongliang Liu, Bo Han, 2022, International Conference on Learning Representations.

Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors

Joshua Y. Kim, Tongliang Liu, K. Yacef, 2022, ICMI Companion.

Advances in data representation and learning for pattern analysis

Fionn Murtagh, Weifeng Liu, C. L. Philip Chen, 2019, Neurocomputing.

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

Xiansheng Hua, Tongliang Liu, Xinchao Wang, 2021, ACM Multimedia.

Watermarking for Out-of-distribution Detection

Tongliang Liu, Yonggang Zhang, Qizhou Wang, 2022, NeurIPS.

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

Masashi Sugiyama, M. Sugiyama, Mingming Gong, 2021, International Conference on Learning Representations.

A Machine Learning Approach for Predicting Human Preference for Graph Drawings

Tongliang Liu, Shijun Cai, Seok-Hee Hong, 2022 .

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Mingming Gong, Kun Zhang, Tongliang Liu, 2022, ArXiv.

Learning from Noisy Pairwise Similarity and Unlabeled Data

Masashi Sugiyama, Pradeep Ravikumar, M. Sugiyama, 2022, J. Mach. Learn. Res..

FedDAG: Federated DAG Structure Learning

Mingming Gong, Tongliang Liu, H. Bondell, 2021, 2112.03555.

To Smooth or Not? When Label Smoothing Meets Noisy Labels

Tongliang Liu, Gang Niu, Jiaheng Wei, 2021, ICML.

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

Tongliang Liu, Jing Zhang, Juhua Liu, 2022, ArXiv.

NoiLin: Improving adversarial training and correcting stereotype of noisy labels

Masashi Sugiyama, M. Sugiyama, Tongliang Liu, 2021, Trans. Mach. Learn. Res..

FedDAG: Federated DAG Structure Learning

Mingming Gong, Tongliang Liu, H. Bondell, 2021, Trans. Mach. Learn. Res..

Learning relative features through adaptive pooling for image classification

Ming Shao, Yun Fu, Thomas S. Huang, 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

Tongliang Liu, James Cheng, Han Yang, 2022, ICLR.

Orthogonal Deep Neural Networks

Dacheng Tao, Tongliang Liu, Kui Jia, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence

Junjie Wu, Yun Fu, Hongfu Liu, 2017, IEEE Transactions on Knowledge and Data Engineering.

Probabilistic Margins for Instance Reweighting in Adversarial Training

Masashi Sugiyama, Mingyuan Zhou, M. Sugiyama, 2021, NeurIPS.

Truncated Cauchy Non-Negative Matrix Factorization

Larry S. Davis, Dacheng Tao, Tongliang Liu, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space

Dacheng Tao, Xinmei Tian, Tongliang Liu, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

Masashi Sugiyama, Jingfeng Zhang, Tongliang Liu, 2020, ICML.

Parts-dependent Label Noise: Towards Instance-dependent Label Noise pdf

Gang Niu, Nannan Wang, Tongliang Liu, 2020, ArXiv.

Harnessing Side Information for Classification Under Label Noise

Chen Gong, Dacheng Tao, Tongliang Liu, 2020, IEEE Transactions on Neural Networks and Learning Systems.

General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer

Yong Luo, Yonggang Wen, Dacheng Tao, 2017, IJCAI.

Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing

Heng Tao Shen, Dacheng Tao, Tongliang Liu, 2016, IEEE Transactions on Image Processing.

Towards Mixture Proportion Estimation without Irreducibility pdf

Gang Niu, Yu Yao, Masashi Sugiyama, 2020, ArXiv.

Learning with Biased Complementary Labels

Dacheng Tao, Mingming Gong, Tongliang Liu, 2017, ECCV.

Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification

Qingshan Liu, Dacheng Tao, Tongliang Liu, 2016, IEEE Transactions on Image Processing.

LTF: A Label Transformation Framework for Correcting Label Shift

Dacheng Tao, Tongliang Liu, Mingming Gong, 2020, ICML.

Extended

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: Learning With Mixed Closed-Set and Open-Set Noisy Labels pdf

Nannan Wang, Tongliang Liu, Bo Han, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Decomposition-Based Transfer Distance Metric Learning for Image Classification

Yong Luo, Dacheng Tao, Chao Xu, 2014, IEEE Transactions on Image Processing.

Revisiting Knowledge Distillation: An Inheritance and Exploration Framework

Bing Deng, Xinmei Tian, Xu Shen, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Why ResNet Works? Residuals Generalize

Dacheng Tao, Tongliang Liu, Fengxiang He, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Learning with Bounded Instance- and Label-dependent Label Noise

Kotagiri Ramamohanarao, Dacheng Tao, Tongliang Liu, 2017, ICML.

Multiclass Learning With Partially Corrupted Labels

Dacheng Tao, Tongliang Liu, Ruxin Wang, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Symmetric Pruning in Quantum Neural Networks

Tongliang Liu, Dacheng Tao, Yuxuan Du, 2022, ICLR.

On the learnability of quantum neural networks

Dacheng Tao, Min-Hsiu Hsieh, Tongliang Liu, 2020, 2007.12369.

The Expressive Power of Parameterized Quantum Circuits pdf

Dacheng Tao, Tongliang Liu, Min-Hsiu Hsieh, 2018, ArXiv.

Representative Vector Machines: A Unified Framework for Classical Classifiers

Tieniu Tan, Zhenan Sun, Jie Gui, 2016, IEEE Transactions on Cybernetics.

Algorithm-Dependent Generalization Bounds for Multi-Task Learning

Dacheng Tao, Mingli Song, Tongliang Liu, 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Experimental Quantum Generative Adversarial Networks for Image Generation pdf

Dacheng Tao, Xiaobo Zhu, Cheng-Zhi Peng, 2020, Physical Review Applied.

HumanMAC: Masked Motion Completion for Human Motion Prediction

Tongliang Liu, Ling Chen, Xiaobo Xia, 2023, ArXiv.

Exploring Language Hierarchy for Video Grounding

Tongliang Liu, Shiwei Zhang, N. Wang, 2022, IEEE Transactions on Image Processing.

CRIS: CLIP-Driven Referring Image Segmentation

Tongliang Liu, Qiang Li, Zhaoqing Wang, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Domain Generalization via Conditional Invariant Representations

Dacheng Tao, Ya Li, Mingming Gong, 2018, AAAI.

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Tongliang Liu, Gang Niu, Jiaheng Wei, 2021, ICLR.

Understanding the Interaction of Adversarial Training with Noisy Labels pdf

Hongxia Yang, Masashi Sugiyama, Jingfeng Zhang, 2021, ArXiv.

On Compressing Deep Models by Low Rank and Sparse Decomposition

Dacheng Tao, Tongliang Liu, Xinchao Wang, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels pdf

Mingming Gong, Masashi Sugiyama, Tongliang Liu, 2021, ICLR.

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Zhou Wang, Dacheng Tao, Tongliang Liu, 2017, IEEE Transactions on Image Processing.

HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking

Bo Du, Mingming Gong, Chen Wu, 2021, IEEE Transactions on Image Processing.

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence

Dacheng Tao, Tongliang Liu, Fengxiang He, 2019, NeurIPS.

Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

Dwarikanath Mahapatra, Mehrtash Harandi, Tom Drummond, 2021, ArXiv.

Fast Supervised Discrete Hashing

Zhenan Sun, Tieniu Tan, Dacheng Tao, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Mingming Gong, Tongliang Liu, Bo Han, 2022, ICLR.

Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval

Yong Luo, Han Hu, Yonggang Wen, 2020, ACM Multimedia.

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

Tongliang Liu, Bo Han, Quanming Yao, 2023, ICLR.

Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces

Stefanos Zafeiriou, Jia Guo, Tongliang Liu, 2020, ECCV.

Online Heterogeneous Transfer Metric Learning

Yong Luo, Yonggang Wen, Dacheng Tao, 2018, IJCAI.

Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation

Dacheng Tao, Cheng Deng, Tongliang Liu, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Spectral Ensemble Clustering

Junjie Wu, Yun Fu, Hongfu Liu, 2015, KDD.

Unsupervised Semantic-Preserving Adversarial Hashing for Image Search

Jie Li, Dacheng Tao, Cheng Deng, 2019, IEEE Transactions on Image Processing.

Understanding and Improving Early Stopping for Learning with Noisy Labels pdf

Erkun Yang, Yanhua Yang, Tongliang Liu, 2021, NeurIPS.

An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption

Dacheng Tao, Mingming Gong, Kayhan Batmanghelich, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation

Tongliang Liu, Wankou Yang, Junjie Ye, 2023, Trans. Mach. Learn. Res..

Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval

D. Tao, Tongliang Liu, Cheng Deng, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

Tongliang Liu, Jing Zhang, Juhua Liu, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration

Tongliang Liu, Songhua Wu, Min Xu, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC

Tongliang Liu, Jiankang Deng, Ziyong Feng, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency

Tongliang Liu, H. Yao, Kai Wang, 2023, arXiv.org.

Robust early-learning: Hindering the memorization of noisy labels

Chen Gong, Yi Chang, Zongyuan Ge, 2021, ICLR.

Are Anchor Points Really Indispensable in Label-Noise Learning?

Gang Niu, Masashi Sugiyama, Chen Gong, 2019, NeurIPS.

Removing Adversarial Noise in Class Activation Feature Space pdf

Xinbo Gao, Dawei Zhou, Tongliang Liu, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Adversarial Examples for Hamming Space Search

Dacheng Tao, Cheng Deng, Tongliang Liu, 2020, IEEE Transactions on Cybernetics.

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network

Tongliang Liu, Bo Han, Gang Niu, 2021, ICML.

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

Tongliang Liu, Bo Han, Xiaobo Xia, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation pdf

Chen Gong, Tongliang Liu, Chenhong Zhou, 2021, ArXiv.

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment pdf

Yong Yin, Xiuying Wang, Tongliang Liu, 2020, Pattern Recognition.

Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning

Tongliang Liu, Chun Yuan, Zhenyi Wang, 2023, ArXiv.

Exploring Set Similarity for Dense Self-supervised Representation Learning

Mingming Gong, Tongliang Liu, Guoxin Zhang, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Estimating Instance-dependent Label-noise Transition Matrix using DNNs pdf

Erkun Yang, Tongliang Liu, Yang Liu, 2021, ArXiv.

Class2Simi: A New Perspective on Learning with Label Noise pdf

Gang Niu, Nannan Wang, Tongliang Liu, 2020, ArXiv.

Learning with Group Noise

Hongxia Yang, Chen Gong, Mingming Gong, 2021, AAAI.

Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver.

Tongliang Liu, Xu-Fei Yin, Yu-Ao Chen, 2022, Physical review letters.

A Survey of Label-noise Representation Learning: Past, Present and Future pdf

Ivor W. Tsang, James T. Kwok, Tongliang Liu, 2020, ArXiv.

Towards Lightweight Black-Box Attacks against Deep Neural Networks

Tongliang Liu, Ya Li, Xinmei Tian, 2022, ArXiv.

Invariance Principle Meets Out-of-Distribution Generalization on Graphs

Tongliang Liu, James Cheng, Han Yang, 2022, ArXiv.

Adversarial Robustness through the Lens of Causality pdf

Bernhard Schölkopf, Mingming Gong, Xinmei Tian, 2022, ICLR.

Confidence Scores Make Instance-dependent Label-noise Learning Possible

Gang Niu, Masashi Sugiyama, Tongliang Liu, 2019, ICML.

Local Rademacher Complexity for Multi-Label Learning

Dacheng Tao, Chao Xu, Tongliang Liu, 2014, IEEE Transactions on Image Processing.

A Regularization Approach for Instance-Based Superset Label Learning

Jian Yang, Dacheng Tao, Chen Gong, 2018, IEEE Transactions on Cybernetics.

Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation

Jian Yang, D. Tao, Tongliang Liu, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Understanding Robust Overfitting of Adversarial Training and Beyond

Mingming Gong, Tongliang Liu, Bo Han, 2022, ICML.

Counterfactual Fairness with Partially Known Causal Graph

Mingming Gong, Kun Zhang, Tongliang Liu, 2022, NeurIPS.

Robust Weight Perturbation for Adversarial Training

Tongliang Liu, Bo Han, Shiming Ge, 2022, IJCAI.

MODELING ADVERSARIAL NOISE FOR ADVERSARIAL DEFENSE

Tongliang Liu, Bo Han, Dawei Zhou, 2021 .

Reliable Adversarial Distillation with Unreliable Teachers

Jingren Zhou, Tongliang Liu, Bo Han, 2021, ICLR.

Algorithmic Stability and Hypothesis Complexity

Dacheng Tao, Tongliang Liu, Gábor Lugosi, 2017, ICML.

Absent Multiple Kernel Learning Algorithms

Yong Dou, Xinwang Liu, En Zhu, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes

Dong Xu, Dacheng Tao, Tongliang Liu, 2016, Neural Computation.

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds

Tongliang Liu, Jiankang Deng, Xiaobo Xia, 2023, 2306.03116.

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning

Tongliang Liu, Han Zhang, Xiaobo Xia, 2022, NeurIPS.

A Second-Order Approach to Learning with Instance-Dependent Label Noise pdf

Yang Liu, Tongliang Liu, Zhaowei Zhu, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Understanding (Generalized) Label Smoothing when Learning with Noisy Labels

Tongliang Liu, Gang Niu, Jiaheng Wei, 2021, ArXiv.

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Dacheng Tao, Cheng Deng, Wei Liu, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Provably End-to-end Label-Noise Learning without Anchor Points

Masashi Sugiyama, M. Sugiyama, Tongliang Liu, 2021, ICML.

Supervised Discrete Hashing With Relaxation

Tieniu Tan, Zhenan Sun, Dacheng Tao, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

Yixuan Li, Junzhou Huang, Tongliang Liu, 2021, 2106.07451.

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Mingming Gong, Kun Zhang, Tongliang Liu, 2022, NeurIPS.

Federated Causal Discovery pdf

Mingming Gong, Tongliang Liu, Erdun Gao, 2021, ArXiv.

Multiview Matrix Completion for Multilabel Image Classification

Yong Luo, Dacheng Tao, Chao Xu, 2015, IEEE Transactions on Image Processing.

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning pdf

Gang Niu, Yu Yao, Tongliang Liu, 2020, NeurIPS.

Towards Label-free Scene Understanding by Vision Foundation Models

Tongliang Liu, Wenping Wang, Yuexin Ma, 2023, ArXiv.

Demystifying Assumptions in Learning to Discover Novel Classes

Long Lan, Masashi Sugiyama, Tongliang Liu, 2021 .

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

Ya Li, Xinmei Tian, Yonggang Zhang, 2020, ICML.

Exploring Model Dynamics for Accumulative Poisoning Discovery

Liang Wang, Tongliang Liu, Bo Han, 2023, arXiv.org.

Strength-Adaptive Adversarial Training

Mingming Gong, Tongliang Liu, Bo Han, 2022, arXiv.org.

Learning Diverse-Structured Networks for Adversarial Robustness pdf

Gang Niu, Junzhou Huang, Masashi Sugiyama, 2021, ICML.

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? pdf

Masashi Sugiyama, Lizhen Cui, Jingfeng Zhang, 2021, ArXiv.

Selective-Supervised Contrastive Learning with Noisy Labels

Tongliang Liu, Xiaobo Xia, Shiming Ge, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Instance-Dependent PU Learning by Bayesian Optimal Relabeling pdf

Geoffrey I. Webb, Dacheng Tao, Tongliang Liu, 2018, ArXiv.

Fair Classification with Instance-dependent Label Noise

Mingming Gong, Tongliang Liu, Bo Han, 2022, CLeaR.

A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection

Zhe Chen, Dacheng Tao, Wanli Ouyang, 2019, International Journal of Computer Vision.

Semantic Structure-based Unsupervised Deep Hashing

Wei Liu, Dacheng Tao, Cheng Deng, 2018, IJCAI.

Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering

Tongliang Liu, Jun Liu, Yaxian Wang, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels

Mingming Gong, Tongliang Liu, Bo Han, 2020, ICML.

Quantum noise protects quantum classifiers against adversaries pdf

Dacheng Tao, Min-Hsiu Hsieh, Tongliang Liu, 2020, Physical Review Research.

Out-of-distribution Detection with Implicit Outlier Transformation

Tongliang Liu, Bo Han, Feng Liu, 2023, ICLR.

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation

Masashi Sugiyama, M. Sugiyama, Mingming Gong, 2023, ICML.

Correcting the Triplet Selection Bias for Triplet Loss

Dacheng Tao, Mingming Gong, Tongliang Liu, 2018, ECCV.

Bayesian Quantum Circuit

Dacheng Tao, Tongliang Liu, Yuxuan Du, 2018, 1805.11089.

Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization

Dacheng Tao, Tongliang Liu, Runyao Duan, 2018, IJCAI.

Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

Zhou Wang, Dacheng Tao, Tongliang Liu, 2016, IEEE Transactions on Image Processing.

Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search

Dacheng Tao, Tongliang Liu, Erkun Yang, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

Dacheng Tao, Kede Ma, Zhou Wang, 2016, ArXiv.

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer

Tongliang Liu, Rongrong Ji, Yiyi Zhou, 2023, ArXiv.

A Quantum-inspired Algorithm for General Minimum Conical Hull Problems pdf

Dacheng Tao, Tongliang Liu, Min-Hsiu Hsieh, 2019, Physical Review Research.

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Xiaohong Jia, Asoke K. Nandi, Tongliang Liu, 2019, IEEE Transactions on Image Processing.

Label-Noise Robust Domain Adaptation

Dacheng Tao, Kun Zhang, Kayhan Batmanghelich, 2020, ICML.

A H OLISTIC V IEW OF L ABEL N OISE T RANSITION M A - TRIX IN D EEP L EARNING AND B EYOND

Tongliang Liu, Bo Han, Weizhong Zhang, 2023 .

HumanMAC: Masked Motion Completion for Human Motion Prediction

Tongliang Liu, Ling Chen, Xiaobo Xia, 2023, ArXiv.

Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering

Tingbo Hou, Tongliang Liu, Huan Fu, 2023, ArXiv.

Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning

Tongliang Liu, Chun Yuan, Zhenyi Wang, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Positive and Unlabeled Learning with Label Disambiguation

Tongliang Liu, Jian Yang, Chuang Zhang, 2019, IJCAI.

Understanding How Feature Structure Transfers in Transfer Learning

Qiang Yang, Dacheng Tao, Tongliang Liu, 2017, IJCAI.

Generative-Discriminative Complementary Learning

Kun Zhang, Mingming Gong, Tongliang Liu, 2019, AAAI.

Local Rademacher Complexity for Multi-label Learning Local Rademacher Complexity for Multi-label Learning

D. Tao, Tongliang Liu, Chang Xu, 2014 .

Multi-View Matrix Completion for Multi-Label Image Classification pdf

Yong Luo, Chao Xu, Dacheng Tao, 2019, ArXiv.

Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning

Dacheng Tao, Hong Shi, Tongliang Liu, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deformed Graph Laplacian for Semisupervised Learning

Dacheng Tao, Jie Yang, Chen Gong, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Exploiting Class Activation Value for Partial-Label Learning

Masashi Sugiyama, M. Sugiyama, Tongliang Liu, 2022, ICLR.

HumanMAC: Masked Motion Completion for Human Motion Prediction

Tongliang Liu, Ling Chen, Xiaobo Xia, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning

Tongliang Liu, Bo Han, Xiaobo Xia, 2023, ICLR.

Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE

Tongliang Liu, Xiaobo Xia, Bo An, 2022, NeurIPS.

Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation

Tongliang Liu, Shiming Ge, Liansheng Zhuang, 2023, ArXiv.

SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation

Tongliang Liu, Jie Liu, Yiyuan Yuan, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

Tongliang Liu, Yibing Zhan, Chuang Liu, 2023, Neural Networks.

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Tongliang Liu, Jia Wu, Dacheng Tao, 2022, IJCAI.

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Masashi Sugiyama, Tongliang Liu, Bo Han, 2023, ArXiv.

Large-Cone Nonnegative Matrix Factorization

Dacheng Tao, Mingming Gong, Tongliang Liu, 2017, IEEE Transactions on Neural Networks and Learning Systems.

No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model

Zhiguo Jiang, Dacheng Tao, Tongliang Liu, 2015, IEEE Signal Processing Letters.

Continuous Dropout

Dacheng Tao, Fang Xu, Xinmei Tian, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data

Tongliang Liu, Ying-Long Bai, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition pdf

Dwarikanath Mahapatra, Tom Drummond, Tongliang Liu, 2021, MICCAI.

KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization

Tongliang Liu, Jie Li, Xinbo Gao, 2021, IEEE Transactions on Image Processing.

Weakly Supervised Temporal Action Localization with Segment-Level Labels pdf

Jie Li, Xiaoyu Wang, Xinbo Gao, 2020, PRCV.

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

Tongliang Liu, Shiming Chen, Ziming Hong, 2023, ICML.

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Tongliang Liu, Juhua Liu, Bo Du, 2023, ArXiv.

On the Performance of Manhattan Nonnegative Matrix Factorization

Dacheng Tao, Tongliang Liu, D. Tao, 2016, IEEE Transactions on Neural Networks and Learning Systems.

On the Performance of Manhattan Non-negative Matrix Factorization

Dacheng Tao, Tongliang Liu, D. Tao, 2016 .

ShadowNet for Data-Centric Quantum System Learning

Zhouchen Lin, Tongliang Liu, Bernard Ghanem, 2023, ArXiv.

Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

Jian Yang, Chen Gong, Dacheng Tao, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain

Yong Luo, Yonggang Wen, Dacheng Tao, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Understanding How Pretraining Regularizes Deep Learning Algorithms

Tongliang Liu, Chen Gong, Yu Yao, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Transfer Learning with Label Noise

Kun Zhang, Dacheng Tao, Mingming Gong, 2017, 1707.09724.

Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm

Yong Luo, Ling-Yu Duan, Shiqi Wang, 2020, IEEE Transactions on Multimedia.

An Optimal Transport View on Generalization pdf

Dacheng Tao, Tongliang Liu, Jingwei Zhang, 2018, ArXiv.

A Universal Unbiased Method for Classification from Aggregate Observations

Tongliang Liu, Bo Han, Gang Niu, 2023, ICML.

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

Masashi Sugiyama, Tongliang Liu, Bo Han, 2023, ArXiv.

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization

Tongliang Liu, Bo Han, Yuxuan Du, 2022, NeurIPS.

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model pdf

Gang Niu, Chen Gong, Tongliang Liu, 2021, AAAI.

Instance Correction for Learning with Open-set Noisy Labels pdf

Mingming Gong, Masashi Sugiyama, Tongliang Liu, 2021, ArXiv.

An Optimal Transport Analysis on Generalization in Deep Learning

D. Tao, Tongliang Liu, Jingwei Zhang, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Multi-Task Model and Feature Joint Learning

Dacheng Tao, Ya Li, Xinmei Tian, 2015, IJCAI.

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond

Tongliang Liu, Bo Han, Weizhong Zhang, 2023, ICLR.

Fairness Improves Learning from Noisily Labeled Long-Tailed Data

Masashi Sugiyama, Tongliang Liu, Gang Niu, 2023, ArXiv.

Regularly Truncated M-Estimators for Learning With Noisy Labels

Tongliang Liu, Bo Han, Xiaobo Xia, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Late Stopping: Avoiding Confidently Learning from Mislabeled Examples

Tongliang Liu, Lei Feng, Suqin Yuan, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Multi-Class Classification from Noisy-Similarity-Labeled Data pdf

Gang Niu, Tongliang Liu, Bo Han, 2020, ArXiv.

On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning

Dacheng Tao, Ya Li, Xinmei Tian, 2018, IEEE Transactions on Neural Networks and Learning Systems.

An Information-Theoretic View for Deep Learning pdf

Dacheng Tao, Jingwei Zhang, Tongliang Liu, 2018, ArXiv.

On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier pdf

Dacheng Tao, Jingwei Zhang, Tongliang Liu, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Label-Noise Robust Domain Adaptation Supplementary Material

D. Tao, Mingming Gong, Kun Zhang, 2020 .

Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

D. Tao, Tongliang Liu, Zhou Wang, 2016, ArXiv.

Towards Defending against Adversarial Examples via Attack-Invariant Features pdf

Xinbo Gao, Dawei Zhou, Tongliang Liu, 2021, ICML.

Channel-Wise Contrastive Learning for Learning with Noisy Labels

Tongliang Liu, Sheng Liu, Huaxi Huang, 2023, ArXiv.

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

Tongliang Liu, Bo Han, Dadong Wang, 2023, ArXiv.

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds

Tongliang Liu, Jiankang Deng, Xiaobo Xia, 2023, IEEE transactions on pattern analysis and machine intelligence.

BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency

Tongliang Liu, H. Yao, Kai Wang, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Dynamics-Aware Loss for Learning with Label Noise

Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu, 2023, Pattern Recognit..

PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels

Tongliang Liu, Olivier Salvado, Dadong Wang, 2022, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Learning and Mining with Noisy Labels

Masashi Sugiyama, Tongliang Liu, Bo Han, 2022, CIKM.

Trustable Co-Label Learning From Multiple Noisy Annotators

Tongliang Liu, Shiming Ge, Dan Zeng, 2022, IEEE Transactions on Multimedia.

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Mingming Gong, Tongliang Liu, Bo Han, 2022, ArXiv.

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

Mingming Gong, Tongliang Liu, Bo Han, 2023, ICML.

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels pdf

Junzhou Huang, Yu Rong, Tingyang Xu, 2021, ArXiv.

Classification with Noisy Labels by Importance Reweighting

D. Tao, Tongliang Liu, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Repulsive Mixture Models of Exponential Family PCA for Clustering pdf

Dacheng Tao, Maoying Qiao, Jun Yu, 2020, ArXiv.

LR-SVM+: Learning Using Privileged Information with Noisy Labels

Ruxin Wang, Tongliang Liu, Zhengning Wu, 2022, IEEE Transactions on Multimedia.

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Tongliang Liu, Juhua Liu, Bo Du, 2023, ACM Multimedia.

Laplacian Welsch Regularization for Robust Semisupervised Learning

Dacheng Tao, Chen Gong, Jian Yang, 2020, IEEE Transactions on Cybernetics.

Video Face Editing Using Temporal-Spatial-Smooth Warping

Xiaoyan Li, Tongliang Liu, Dacheng Tao, 2014, ACM Trans. Intell. Syst. Technol..

SELF-SUPERVISED VISUAL PRE-TRAINING

Mingming Gong, Tongliang Liu, Yandong Guo, 2023 .

Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation

Tongliang Liu, Yixuan Yuan, Jie Liu, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Where is the Bottleneck of Adversarial Learning with Unlabeled Data? pdf

Gang Niu, Masashi Sugiyama, Bo Han, 2019, ArXiv.

ALIP: Adaptive Language-Image Pre-training with Synthetic Caption

Tongliang Liu, Jiankang Deng, Ziyong Feng, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Why do CNNs excel at feature extraction? A mathematical explanation

Tongliang Liu, V. Nandakumar, Arush Tagade, 2023, ArXiv.

Expressive power of parametrized quantum circuits

Tongliang Liu, Min-Hsiu Hsieh, Dacheng Tao, 2020, Physical Review Research.

Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering

Tingbo Hou, Tongliang Liu, Huan Fu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

Masashi Sugiyama, Tongliang Liu, Bo Han, 2020, ICML.

Towards Label-free Scene Understanding by Vision Foundation Models

Tongliang Liu, Wenping Wang, Yuexin Ma, 2023, ArXiv.

Ieee Transactions on Pattern Analysis and Machine Intelligence Classification with Noisy Labels by Importance Reweighting

D. Tao, Tongliang Liu, 2022 .

Mosaic Representation Learning for Self-supervised Visual Pre-training

Mingming Gong, Tongliang Liu, Yandong Guo, 2023, ICLR.

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning

Tongliang Liu, Bo Han, Erkun Yang, 2022, NeurIPS.

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

Tongliang Liu, Bo Han, Feng Liu, 2021, Trans. Mach. Learn. Res..

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

Tongliang Liu, Bo Han, Feng Liu, 2021, Trans. Mach. Learn. Res..

Probabilistic Margins for Instance Reweighting in Adversarial Training

Masashi Sugiyama, Mingyuan Zhou, Tongliang Liu, 2021, NeurIPS.

Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths

Tongliang Liu, Chenhao Lin, Q. Li, 2023, ArXiv.

Type-I Generative Adversarial Attack

Tongliang Liu, Wei Zhou, Renyang Liu, 2023, IEEE Transactions on Dependable and Secure Computing.

Unicom: Universal and Compact Representation Learning for Image Retrieval

Tongliang Liu, Jiankang Deng, Ziyong Feng, 2023, ICLR.

Reliable Multi-View Clustering

Chenping Hou, Dongyun Yi, Jubo Zhu, 2018, AAAI.

Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors pdf

Kalina Yacef, Tongliang Liu, Joshua Y. Kim, 2021, ArXiv.

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

Masashi Sugiyama, Tongliang Liu, Bo Han, 2022, NeurIPS.

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability

Tongliang Liu, Bo Han, Jiangchao Yao, 2023, ICML.

Continual Learning From a Stream of APIs

Tongliang Liu, Li Shen, Zhenyi Wang, 2023, ArXiv.

Pluralistic Image Completion with Gaussian Mixture Models

Tongliang Liu, Bo Han, J. Ren, 2022, NeurIPS.

Modeling Adversarial Noise for Adversarial Training

Tongliang Liu, Bo Han, Dawei Zhou, 2021, ICML.

Learning and Mining with Noisy Labels

Masashi Sugiyama, Tongliang Liu, Bo Han, 2022, CIKM.

Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration

Tongliang Liu, Yukun Chen, Decheng Liu, 2023, ICML.

Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer

Kazushi Ikeda, Yasutaka Furusho, Tongliang Liu, 2019, INNSBDDL.