Guoqiang Wu
14 papers · 2020–2025 · 5 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+4 more ↓ Show less ↑
π£ Hot Topic Early Bird π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (28)
π
Keyword Champion
(2)
π
Century Club Β· Bronze
β
The Questioner
(2)
ποΈ
Keyword Collector
(81)
Venues
Top co-authors
Research topics
Machine Learning
(13)
Optimization & Theory Β· Machine Learning
(10)
Learning Types Β· Machine Learning
(7)
Deep Learning
(6)
Core Methods
(5)
Artificial Intelligence
(3)
Application Areas
(3)
Core AI
(3)
Models
(3)
Learning Types Β· Deep Learning
(2)
Optimization & Theory Β· Deep Learning
(2)
Natural Language Processing
(1)
Reinforcement Learning
(1)
Architectures
(1)
Generation
(1)
Keywords
multi-label learning
(3)
class imbalance
(3)
generalization bound
(3)
multi-label classification
(2)
hamming loss
(2)
hyperparameter optimization
(2)
surrogate loss
(2)
uniform stability
(2)
few-shot learning
(1)
logistic regression
(1)
semi-supervised learning
(1)
reinforcement learning
(1)
consistency analysis
(1)
transfer learning
(1)
black-box optimization
(1)
adversarial learning
(1)
convergence analysis
(1)
imitation learning
(1)
sample complexity
(1)
multimodal learning
(1)
Papers
A Theory for Conditional Generative Modeling on Multiple Data Sources
ICML 2025
Towards Macro-AUC Oriented Imbalanced Multi-Label Continual Learning
AAAI 2025
DiffAIL: Diffusion Adversarial Imitation Learning
AAAI 2024
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing
EMNLP 2024
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
NeurIPS 2024
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
NeurIPS 2024
Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning?
ACML 2023
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
ICML 2023
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
ICML 2023
Toward Understanding Generative Data Augmentation
NeurIPS 2023
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
NeurIPS 2021
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
NeurIPS 2021
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
NeurIPS 2021
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
NeurIPS 2020