← Authors

Weiwei Liu

60 papers · 2015–2026 · 11 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (9) 🌍 Conference Polyglot (11) πŸƒ Academic Marathon (10) πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🐺 Lone Wolf (3) πŸ”¬ Deep Specialist (17) πŸ† Keyword Champion (4) πŸ—ƒοΈ Keyword Collector (223) πŸš€ Conference Pioneer πŸ’Ž Century Club (58) πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter ⚑ Prolific Year (5)

Conferences

ICML (15) NIPS (13) IJCAI (10) AAAI (9) JMLR (5) ACL (3) COLT (1) CORL (1) CVPR (1) IJCNLP (1) INTERSPEECH (1)

Top co-authors

Zekai Wang (8) Zhengyu Zhou (7) Haobo Wang (6) Tianlei Hu (6) Xin Zou (6) Gang Chen (6) Xinsong Ma (5) Wenbin Hu (5) Yuren Mao (5) Xiaobo Shen (4)

Keywords

adversarial training (7) multi-task learning (7) adversarial robustness (6) multi-label classification (5) generalization bound (5) label correlation (5) generalization error bound (4) text classification (4) representation learning (4) adversarial learning (3) deep neural network (3) multiclass classification (3) convex optimization (3) adversarial attack (3) neural network (3) robust optimization (2) image classification (2) dimensionality reduction (2) sample complexity (2) label propagation (2)

Papers

On the Robustness of Bandit Multiple Testing AAAI 2026
Rademacher Complexity for Distributionally Robust Learning AAAI 2026
An Error Analysis of Flow Matching for Deep Generative Modeling ICML 2025
Model Uncertainty Quantification by Conformal Prediction in Continual Learning ICML 2025
Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks ICML 2025
Nonconvex Theory of $M$-estimators with Decomposable Regularizers ICML 2025
A Closer Look at Generalized BH Algorithm for Out-of-Distribution Detection ICML 2025
An Online Statistical Framework for Out-of-Distribution Detection ICML 2025
DRF: Improving Certified Robustness via Distributional Robustness Framework AAAI 2024
Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data AAAI 2024
Sequential Kernel Goodness-of-fit Testing ICML 2024
A Provable Decision Rule for Out-of-Distribution Detection ICML 2024
A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks ICML 2024
The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems NIPS 2024
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation CVPR 2024
Zero-shot Learning for Preclinical Drug Screening IJCAI 2024
A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers NIPS 2024
Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game NIPS 2024
A Closer Look at Curriculum Adversarial Training: From an Online Perspective AAAI 2024
Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation NIPS 2023
Characterization of Overfitting in Robust Multiclass Classification NIPS 2023
TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning CORL 2023
RVCL: Evaluating the Robustness of Contrastive Learning via Verification JMLR 2023
Generalization Bounds for Adversarial Contrastive Learning JMLR 2023
WAT: Improve the Worst-Class Robustness in Adversarial Training AAAI 2023
Improved Bounds for Multi-task Learning with Trace Norm Regularization COLT 2023
Sample Complexity for Distributionally Robust Learning under chi-square divergence JMLR 2023
DDGR: Continual Learning with Deep Diffusion-based Generative Replay ICML 2023
Better Diffusion Models Further Improve Adversarial Training ICML 2023
Delving into Noisy Label Detection with Clean Data ICML 2023
Deep Partial Multi-Label Learning with Graph Disambiguation IJCAI 2023
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications NIPS 2023
On the Adversarial Robustness of Out-of-distribution Generalization Models NIPS 2023
Defending Against Adversarial Attacks via Neural Dynamic System NIPS 2022
On the Tradeoff Between Robustness and Fairness NIPS 2022
Robustness Verification for Contrastive Learning ICML 2022
On Robust Multiclass Learnability NIPS 2022
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning ACL 2022
BanditMTL: Bandit-based Multi-task Learning for Text Classification ACL 2021
BanditMTL: Bandit-based Multi-task Learning for Text Classification IJCNLP 2021
Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization IJCAI 2020
Temporal Network Embedding with High-Order Nonlinear Information AAAI 2020
Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification AAAI 2020
Tchebycheff Procedure for Multi-task Text Classification ACL 2020
Adaptive Adversarial Multi-task Representation Learning ICML 2020
Opinion Maximization in Social Trust Networks IJCAI 2020
Collaboration Based Multi-Label Propagation for Fraud Detection IJCAI 2020
Learning From Multi-Dimensional Partial Labels IJCAI 2020
Copula Multi-label Learning NIPS 2019
Discriminative and Correlative Partial Multi-Label Learning IJCAI 2019
Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification AAAI 2019
Sparse Extreme Multi-label Learning with Oracle Property ICML 2019
Ranking Preserving Nonnegative Matrix Factorization IJCAI 2018
Deep Discrete Prototype Multilabel Learning IJCAI 2018
Discrete Network Embedding IJCAI 2018
An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels JMLR 2017
Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions JMLR 2017
Sparse Embedded $k$-Means Clustering NIPS 2017
THU-EE System Description for NIST LRE 2015 INTERSPEECH 2016
On the Optimality of Classifier Chain for Multi-label Classification NIPS 2015