Zhun Deng
23 papers · 2020–2025 · 6 venues · across top CS/AI venues
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Machine Learning
(20)
Optimization & Theory Β· Machine Learning
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Learning Types Β· Machine Learning
(12)
Artificial Intelligence
(9)
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Core AI
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Deep Learning
(7)
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Reinforcement Learning
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large language model
(4)
data augmentation
(3)
adversarial robustness
(3)
semi-supervised learning
(3)
neural network
(3)
risk management
(3)
adversarial training
(3)
representation learning
(3)
domain adaptation
(2)
contrastive learning
(2)
algorithmic fairness
(2)
statistical learning
(2)
generalization bound
(2)
confidence interval
(2)
loss distribution
(2)
statistical analysis
(1)
transfer learning
(1)
policy optimization
(1)
robust optimization
(1)
knowledge transfer
(1)
Papers
Conformal Tail Risk Control for Large Language Model Alignment
ICML 2025
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
ICML 2025
Synergistic Weak-Strong Collaboration by Aligning Preferences
ACL 2025
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
ICLR 2024
Learning and Forgetting Unsafe Examples in Large Language Models
ICML 2024
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
ICLR 2024
Distribution-Free Statistical Dispersion Control for Societal Applications
NeurIPS 2023
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
ICLR 2023
How Does Information Bottleneck Help Deep Learning?
ICML 2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
NeurIPS 2023
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
ICLR 2023
Reinforcement Learning with Stepwise Fairness Constraints
AISTATS 2023
The Power of Contrast for Feature Learning: A Theoretical Analysis
JMLR 2023
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
AISTATS 2023
An Unconstrained Layer-Peeled Perspective on Neural Collapse
ICLR 2022
Robustness Implies Generalization via Data-Dependent Generalization Bounds
ICML 2022
When and How Mixup Improves Calibration
ICML 2022
Adversarial Training Helps Transfer Learning via Better Representations
NeurIPS 2021
How Does Mixup Help With Robustness and Generalization?
ICLR 2021
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
AISTATS 2021
Toward Better Generalization Bounds with Locally Elastic Stability
ICML 2021
Interpreting Robust Optimization via Adversarial Influence Functions
ICML 2020
Towards Understanding the Dynamics of the First-Order Adversaries
ICML 2020