Yao Li

Orcid: 0000-0002-7195-5774

Affiliations:

University of North Carolina at Chapel Hill, Department of Statistics and Operation Research, NC, USA
University of California, Davis, Department of Statistics, CA, USA (PhD 2020)

According to our database¹, Yao Li authored at least 20 papers between 2017 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

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Bibliography

2024

Adversarial Examples Detection With Bayesian Neural Network.

[BibT_eX]

[DOI]

IEEE Trans. Emerg. Top. Comput. Intell., October, 2024

Trusted Aggregation (TAG): Backdoor Defense in Federated Learning.

[BibT_eX]

[DOI]

Joseph Lavond

Minhao Cheng

Yao Li

Trans. Mach. Learn. Res., 2024

Defense Against Syntactic Textual Backdoor Attacks with Token Substitution.

[BibT_eX]

[DOI]

CoRR, 2024

Improving Logits-based Detector without Logits from Black-box LLMs.

[BibT_eX]

[DOI]

CoRR, 2024

Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability.

[BibT_eX]

[DOI]

IEEE Access, 2024

DALD: Improving Logits-based Detector without Logits from Black-box LLMs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

AdaDiff: Accelerating Diffusion Models Through Step-Wise Adaptive Computation.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation.

[BibT_eX]

[DOI]

CoRR, 2023

Accelerating Dataset Distillation via Model Augmentation.

[BibT_eX]

[DOI]

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

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model.

[BibT_eX]

[DOI]

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

2022

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation.

[BibT_eX]

[DOI]

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

A Review of Adversarial Attack and Defense for Classification Methods.

[BibT_eX]

[DOI]

CoRR, 2021

Detecting Adversarial Examples with Bayesian Neural Network.

[BibT_eX]

[DOI]

CoRR, 2021

Towards Robustness of Deep Neural Networks via Regularization.

[BibT_eX]

[DOI]

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

2020

Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models.

[BibT_eX]

[DOI]

Technometrics, 2020

2019

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding.

[BibT_eX]

[DOI]

CoRR, 2018

Learning from Group Comparisons: Exploiting Higher Order Interactions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Positive-Unlabeled Demand-Aware Recommendation.

[BibT_eX]

[DOI]

CoRR, 2017

Scalable Demand-Aware Recommendation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Yao Li

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