We stand with Ukraine

We stand with Ukraine

Yufeng Li

Orcid: 0000-0002-7727-4304

Affiliations:

Nanjing University, China

According to our database¹, Yufeng Li authored at least 56 papers between 2008 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

On csauthors.net:

Bibliography

2024

Rts: learning robustly from time series data with noisy label.

[BibT_eX]

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Frontiers Comput. Sci., December, 2024

Interactive Reweighting for Mitigating Label Quality Issues.

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IEEE Trans. Vis. Comput. Graph., March, 2024

Robust Test-Time Adaptation for Zero-Shot Prompt Tuning.

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

Safe Abductive Learning in the Presence of Inaccurate Rules.

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

2023

Investigating the Limitation of CLIP Models: The Worst-Performing Categories.

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CoRR, 2023

Parameter-Efficient Long-Tailed Recognition.

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CoRR, 2023

Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions.

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Proceedings of the International Conference on Machine Learning, 2023

Identifying Useful Learnwares for Heterogeneous Label Spaces.

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Proceedings of the International Conference on Machine Learning, 2023

ODS: Test-Time Adaptation in the Presence of Open-World Data Shift.

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Proceedings of the International Conference on Machine Learning, 2023

2022

USB: A Unified Semi-supervised Learning Benchmark.

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Satoshi Nakamura

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Marios Savvides

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Takahiro Shinozaki

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CoRR, 2022

LAMDA-SSL: Semi-Supervised Learning in Python.

[BibT_eX]

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CoRR, 2022

USB: A Unified Semi-supervised Learning Benchmark for Classification.

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Satoshi Nakamura

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Marios Savvides

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Takahiro Shinozaki

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

LOG: Active Model Adaptation for Label-Efficient OOD Generalization.

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

Robust Semi-Supervised Learning when Not All Classes have Labels.

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

Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding.

[BibT_eX]

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Proceedings of the International Conference on Machine Learning, 2022

2021

Interactive Graph Construction for Graph-Based Semi-Supervised Learning.

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IEEE Trans. Vis. Comput. Graph., 2021

Lightweight Label Propagation for Large-Scale Network Data.

[BibT_eX]

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IEEE Trans. Knowl. Data Eng., 2021

Towards Safe Weakly Supervised Learning.

[BibT_eX]

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IEEE Trans. Pattern Anal. Mach. Intell., 2021

STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data.

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

Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment.

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

Abductive Learning with Ground Knowledge Base.

[BibT_eX]

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Stephen H. Muggleton

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

2020

Does Tail Label Help for Large-Scale Multi-Label Learning?

[BibT_eX]

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IEEE Trans. Neural Networks Learn. Syst., 2020

Robust Multi-Label Learning with PRO Loss.

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IEEE Trans. Knowl. Data Eng., 2020

Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement.

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CoRR, 2020

RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift.

[BibT_eX]

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Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data.

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

Semi-Supervised Abductive Learning and Its Application to Theft Judicial Sentencing.

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Proceedings of the 20th IEEE International Conference on Data Mining, 2020

IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience.

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Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness.

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CoRR, 2019

Robust Semi-supervised Representation Learning for Graph-Structured Data.

[BibT_eX]

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Proceedings of the Advances in Knowledge Discovery and Data Mining, 2019

Partial Label Learning with Unlabeled Data.

[BibT_eX]

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

2018

Learning safe multi-label prediction for weakly labeled data.

[BibT_eX]

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Mach. Learn., 2018

Large Margin Graph Construction for Semi-Supervised Learning.

[BibT_eX]

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Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

A General Formulation for Safely Exploiting Weakly Supervised Data.

[BibT_eX]

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Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Learning From Semi-Supervised Weak-Label Data.

[BibT_eX]

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Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learning Safe Prediction for Semi-Supervised Regression.

[BibT_eX]

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Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Graph Quality Judgement: A Large Margin Expedition.

[BibT_eX]

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Towards Safe Semi-Supervised Learning for Multivariate Performance Measures.

[BibT_eX]

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Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Risk Minimization in the Presence of Label Noise.

[BibT_eX]

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Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Towards Making Unlabeled Data Never Hurt.

[BibT_eX]

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IEEE Trans. Pattern Anal. Mach. Intell., 2015

2013

Improved Bounds for the Nyström Method With Application to Kernel Classification.

[BibT_eX]

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Mehrdad Mahdavi

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IEEE Trans. Inf. Theory, 2013

Convex and scalable weakly labeled SVMs.

[BibT_eX]

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J. Mach. Learn. Res., 2013

Multi-Label Learning with PRO Loss.

[BibT_eX]

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Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012

Multi-instance multi-label learning.

[BibT_eX]

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Sheng-Jun Huang

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Artif. Intell., 2012

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison.

[BibT_eX]

[DOI]

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Mehrdad Mahdavi

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Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Towards Discovering What Patterns Trigger What Labels.

[BibT_eX]

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Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

Diversity Regularized Machine.

[BibT_eX]

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Proceedings of the IJCAI 2011, 2011

Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection.

[BibT_eX]

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Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010

S4VM: Safe Semi-Supervised Support Vector Machine

[BibT_eX]

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CoRR, 2010

Cost-Sensitive Semi-Supervised Support Vector Machine.

[BibT_eX]

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Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009

Tighter and Convex Maximum Margin Clustering.

[BibT_eX]

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James Tin-Yau Kwok

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Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A Convex Method for Locating Regions of Interest with Multi-instance Learning.

[BibT_eX]

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Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Multi-instance learning by treating instances as non-I.I.D. samples.

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Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Learning instance specific distances using metric propagation.

[BibT_eX]

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Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Semi-supervised learning using label mean.

[BibT_eX]

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Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

MIML: A Framework for Learning with Ambiguous Objects

[BibT_eX]

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Sheng-Jun Huang

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CoRR, 2008

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