Guoqiang Wu
14 papers · 2020–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Renaissance Researcher (5) πΊοΈ Taxonomy Completionist (31) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (5)
π
Academic Marathon
(5)
π
Cross-Pollinator
(10)
ποΈ
Keyword Collector
(73)
π
Century Club
(14)
β
The Questioner
(2)
Conferences
NIPS (7)
ICML (3)
AAAI (2)
ACML (1)
EMNLP (1)
Top co-authors
Keywords
class imbalance
(3)
generalization bound
(3)
multi-label learning
(3)
uniform stability
(2)
hyperparameter optimization
(2)
surrogate loss
(2)
multi-label classification
(2)
few-shot learning
(1)
semi-supervised learning
(1)
reinforcement learning
(1)
logistic regression
(1)
transfer learning
(1)
consistency analysis
(1)
black-box optimization
(1)
adversarial learning
(1)
convergence analysis
(1)
imitation learning
(1)
multimodal learning
(1)
sample complexity
(1)
in-context learning
(1)
Papers
Towards Macro-AUC Oriented Imbalanced Multi-Label Continual Learning
AAAI 2025
A Theory for Conditional Generative Modeling on Multiple Data Sources
ICML 2025
DiffAIL: Diffusion Adversarial Imitation Learning
AAAI 2024
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
NIPS 2024
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
NIPS 2024
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing
EMNLP 2024
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
ICML 2023
Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning?
ACML 2023
Toward Understanding Generative Data Augmentation
NIPS 2023
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
ICML 2023
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
NIPS 2021
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
NIPS 2021
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
NIPS 2021
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
NIPS 2020