← Authors

Haotao Wang

18 papers · 2019–2024 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (5) 🐝 Cross-Pollinator (10) πŸ—ΊοΈ Taxonomy Completionist (30) 🀝 Dynamic Duo (17) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (52) πŸ’Ž Century Club (18) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter

Conferences

ICLR (6) NIPS (5) ICML (3) AAAI (2) CVPR (1) ECCV (1)

Top co-authors

Zhangyang Wang (17) Jiayu Zhou (6) Junyuan Hong (6) Tianlong Chen (4) Shupeng Gui (4) Ji Liu (4) Aston Zhang (3) Mu Li (2) Shuai Zheng (2) Haichuan Yang (2)

Research topics

Differential Privacy (1)

Keywords

adversarial training (4) adversarial robustness (4) neural network (3) batch normalization (3) model compression (2) anomaly detection (1) neural network security (1) contrastive learning (1) knowledge distillation (1) graph structure (1) data augmentation (1) graph representation learning (1) distributed learning (1) image reconstruction (1) image translation (1) model robustness (1) neural architecture search (1) network pruning (1) ensemble learning (1) link prediction (1)

Papers

Safe and Robust Watermark Injection with a Single OoD Image ICLR 2024
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection ICLR 2023
Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning AAAI 2023
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling NIPS 2023
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization ICLR 2022
Removing Batch Normalization Boosts Adversarial Training ICML 2022
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition ICML 2022
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork NIPS 2022
AugMax: Adversarial Composition of Random Augmentations for Robust Training NIPS 2021
UMEC: Unified model and embedding compression for efficient recommendation systems ICLR 2021
Learning Model-Based Privacy Protection under Budget Constraints AAAI 2021
Troubleshooting Blind Image Quality Models in the Wild CVPR 2021
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference ICLR 2020
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free NIPS 2020
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework ECCV 2020
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively ICLR 2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks ICML 2020
Model Compression with Adversarial Robustness: A Unified Optimization Framework NIPS 2019