Yonggang Zhang

Orcid: 0000-0002-4080-7592

Affiliations:
  • Hong Kong Baptist University, Department of Computer Science, Hong Kong


According to our database1, Yonggang Zhang authored at least 43 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2020
2021
2022
2023
2024
2025
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10
15
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13
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Legend:

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Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2025
Out-of-Distribution Detection with Virtual Outlier Smoothing.
Int. J. Comput. Vis., February, 2025

Characterizing Submanifold Region for Out-of-Distribution Detection.
IEEE Trans. Knowl. Data Eng., January, 2025

Consistent prompt learning for vision-language models.
Knowl. Based Syst., 2025

2024
Expert-level diagnosis of pediatric posterior fossa tumors via consistency calibration.
Knowl. Based Syst., 2024

Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty.
CoRR, 2024

Rethinking Improved Privacy-Utility Trade-off with Pre-existing Knowledge for DP Training.
CoRR, 2024

Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Learning to Shape In-distribution Feature Space for Out-of-distribution Detection.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Component-Level Oracle Bone Inscription Retrieval.
Proceedings of the 2024 International Conference on Multimedia Retrieval, 2024

Interpreting and Improving Large Language Models in Arithmetic Calculation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Robust Training of Federated Models with Extremely Label Deficiency.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

FedImpro: Measuring and Improving Client Update in Federated Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Out-of-Distribution Detection with Negative Prompts.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Federated Learning with Extremely Noisy Clients via Negative Distillation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Invariant Learning via Probability of Sufficient and Necessary Causes.
CoRR, 2023

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training.
CoRR, 2023

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Invariant Learning via Probability of Sufficient and Necessary Causes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SODA: Robust Training of Test-Time Data Adaptors.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Augment Distributions for Out-of-distribution Detection.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Moderately Distributional Exploration for Domain Generalization.
Proceedings of the International Conference on Machine Learning, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Continual Named Entity Recognition without Catastrophic Forgetting.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Hard Sample Matters a Lot in Zero-Shot Quantization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Towards Lightweight Black-Box Attacks against Deep Neural Networks.
CoRR, 2022

Pareto Invariant Risk Minimization.
CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.
CoRR, 2022

Watermarking for Out-of-distribution Detection.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Lightweight Black-Box Attack Against Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning.
Proceedings of the International Conference on Machine Learning, 2022

Adversarial Robustness Through the Lens of Causality.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Meta Convolutional Neural Networks for Single Domain Generalization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Prompt Distribution Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Class-Disentanglement and Applications in Adversarial Detection and Defense.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Principal Component Adversarial Example.
IEEE Trans. Image Process., 2020

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.
Proceedings of the 37th International Conference on Machine Learning, 2020


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