Lei Feng

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Positive and Unlabeled Learning with Controlled Probability Boundary Fence.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sample-specific Masks for Visual Reprogramming-based Prompting.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Early Stopping Against Label Noise Without Validation Data.

[BibT_eX]

[DOI]

Suqin Yuan

Tongliang Liu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Consistent Multi-Class Classification from Multiple Unlabeled Datasets.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Targeted Representation Alignment for Open-World Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Mitigating Underfitting in Learning to Defer with Consistent Losses.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Consistent Hierarchical Classification with A Generalized Metric.

[BibT_eX]

[DOI]

Yuzhou Cao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Learning Geometry-Aware Representations for New Intent Discovery.

[BibT_eX]

[DOI]

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Deep Learning From Multiple Noisy Annotators as A Union.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., December, 2023

Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., November, 2023

Learning cross-domain semantic-visual relationships for transductive zero-shot learning.

[BibT_eX]

[DOI]

Pattern Recognit., September, 2023

COMET: Convolutional Dimension Interaction for Collaborative Filtering.

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., August, 2023

SDFReg: Learning Signed Distance Functions for Point Cloud Registration.

[BibT_eX]

[DOI]

CoRR, 2023

Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios.

[BibT_eX]

[DOI]

CoRR, 2023

DALI: Dynamically Adjusted Label Importance for Noisy Partial Label Learning.

[BibT_eX]

[DOI]

CoRR, 2023

ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning.

[BibT_eX]

[DOI]

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

On the Importance of Feature Separability in Predicting Out-Of-Distribution Error.

[BibT_eX]

[DOI]

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

SPA: A Graph Spectral Alignment Perspective for Domain Adaptation.

[BibT_eX]

[DOI]

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

Binary Classification with Confidence Difference.

[BibT_eX]

[DOI]

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

Regression with Cost-based Rejection.

[BibT_eX]

[DOI]

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

In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer.

[BibT_eX]

[DOI]

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

Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples.

[BibT_eX]

[DOI]

Guowu Yang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

ProMix: Combating Label Noise via Maximizing Clean Sample Utility.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Mitigating Memorization of Noisy Labels by Clipping the Model Prediction.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

A Universal Unbiased Method for Classification from Aggregate Observations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Weakly Supervised Regression with Interval Targets.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Late Stopping: Avoiding Confidently Learning from Mislabeled Examples.

[BibT_eX]

[DOI]

Suqin Yuan

Tongliang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Multi-Label Knowledge Distillation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning.

[BibT_eX]

[DOI]

Guowu Yang

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Fine-Grained Classification with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Consistent Complementary-Label Learning via Order-Preserving Losses.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

A Generalized Unbiased Risk Estimator for Learning with Augmented Classes.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Partial-Label Regression.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Regularized Matrix Factorization for Multilabel Learning With Missing Labels.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2022

Logit Clipping for Robust Learning against Label Noise.

[BibT_eX]

[DOI]

CoRR, 2022

ProMix: Combating Label Noise via Maximizing Clean Sample Utility.

[BibT_eX]

[DOI]

CoRR, 2022

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning.

[BibT_eX]

[DOI]

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

Can Adversarial Training Be Manipulated By Non-Robust Features?

[BibT_eX]

[DOI]

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

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses.

[BibT_eX]

[DOI]

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

Partial Label Learning with Semantic Label Representations.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Mitigating Neural Network Overconfidence with Logit Normalization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Exploiting Class Activation Value for Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

PiCO: Contrastive Label Disambiguation for Partial Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Who Is Your Right Mixup Partner in Positive and Unlabeled Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

With False Friends Like These, Who Can Notice Mistakes?

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Advanced topics in weakly supervised learning

[BibT_eX]

[DOI]

PhD thesis, 2021

Partial multi-label learning with mutual teaching.

[BibT_eX]

[DOI]

Yan Yan

Shining Li

Knowl. Based Syst., 2021

GLIMG: Global and local item graphs for top-N recommender systems.

[BibT_eX]

[DOI]

Inf. Sci., 2021

Embedding-Augmented Generalized Matrix Factorization for Recommendation With Implicit Feedback.

[BibT_eX]

[DOI]

IEEE Intell. Syst., 2021

Multi-Class Classification from Single-Class Data with Confidences.

[BibT_eX]

[DOI]

CoRR, 2021

On the Robustness of Average Losses for Partial-Label Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Provable Defense Against Delusive Poisoning.

[BibT_eX]

[DOI]

CoRR, 2021

Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence.

[BibT_eX]

[DOI]

Deng-Bao Wang

Min-Ling Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Multiple-Instance Learning from Similar and Dissimilar Bags.

[BibT_eX]

[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Learning from Complementary Labels via Partial-Output Consistency Regularization.

[BibT_eX]

[DOI]

Deng-Bao Wang

Min-Ling Zhang

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

Pointwise Binary Classification with Pairwise Confidence Comparisons.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning from Similarity-Confidence Data.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

MetaInfoNet: Learning Task-Guided Information for Sample Reweighting.

[BibT_eX]

[DOI]

CoRR, 2020

COMET: Convolutional Dimension Interaction for Deep Matrix Factorization.

[BibT_eX]

[DOI]

CoRR, 2020

Incorporating Multiple Cluster Centers for Multi-Label Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Provably Consistent Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Discovering Latent Class Labels for Multi-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Can Cross Entropy Loss Be Robust to Label Noise?

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Progressive Identification of True Labels for Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning with Multiple Complementary Labels.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Learning from Multiple Complementary Labels.

[BibT_eX]

[DOI]

CoRR, 2019

EXACT: Attributed Entity Extraction By Annotating Texts.

[BibT_eX]

[DOI]

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

Partial Label Learning by Semantic Difference Maximization.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Fast Top-N Personalized Recommendation on Item Graph.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Collaboration Based Multi-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Partial Label Learning with Self-Guided Retraining.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Leveraging Latent Label Distributions for Partial Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Estimating Latent Relative Labeling Importances for Multi-label Learning.

[BibT_eX]

[DOI]