Haotao Wang

Orcid: 0000-0002-0626-2058

According to our database1, Haotao Wang authored at least 29 papers between 2019 and 2023.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
Troubleshooting image segmentation models with human-in-the-loop.
Mach. Learn., March, 2023

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts.
Trans. Mach. Learn. Res., 2023

Taxonomy of Machine Learning Safety: A Survey and Primer.
ACM Comput. Surv., 2023

Safe and Robust Watermark Injection with a Single OoD Image.
CoRR, 2023

Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling.
CoRR, 2023

Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.
Proceedings of the International Conference on Machine Learning, 2022

Removing Batch Normalization Boosts Adversarial Training.
Proceedings of the International Conference on Machine Learning, 2022

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

AutoMARS: Searching to Compress Multi-Modality Recommendation Systems.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021
Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning.
CoRR, 2021

Practical Machine Learning Safety: A Survey and Primer.
CoRR, 2021

AugMax: Adversarial Composition of Random Augmentations for Robust Training.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

UMEC: Unified model and embedding compression for efficient recommendation systems.
Proceedings of the 9th International Conference on Learning Representations, 2021

Troubleshooting Blind Image Quality Models in the Wild.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Learning Model-Based Privacy Protection under Budget Constraints.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively.
Proceedings of the 8th International Conference on Learning Representations, 2020

Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference.
Proceedings of the 8th International Conference on Learning Representations, 2020

GAN Slimming: All-in-One GAN Compression by a Unified Optimization Framework.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset.
CoRR, 2019

Adversarially Trained Model Compression: When Robustness Meets Efficiency.
CoRR, 2019

Real-Time Rogue ONU Identification with 1D-CNN-Based Optical Spectrum Analysis for Secure PON.
Proceedings of the Optical Fiber Communications Conference and Exhibition, 2019

Model Compression with Adversarial Robustness: A Unified Optimization Framework.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


  Loading...