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← Authors

Linjun Zhang

30 papers · 2020–2025 · 7 conferences · across top CS/AI conferences

Achievements

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+11 more ↓ 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (19) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7)
🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) 🧭 Keyword Pioneer 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (95) ❓ The Questioner ⚑ Prolific Year (7) πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (6) πŸ“ˆ Trend Setter

Conferences

ICML (10) NIPS (7) ICLR (6) AISTATS (4) ACL (1) EMNLP (1) JMLR (1)

Top co-authors

James Zou (13) Huaxiu Yao (11) Zhun Deng (10) Chelsea Finn (4) James Y Zou (3) Kenji Kawaguchi (3) Ryumei Nakada (3) Tianze Wang (3) Chenhang Cui (2) Shuhang Lin (2)

Research topics

Probability (1)

Keywords

representation learning (4) contrastive learning (3) semi-supervised learning (3) large language model (3) data augmentation (3) domain generalization (3) preference optimization (2) adversarial training (2) retrieval-augmented generation (2) spurious correlation (2) multimodal learning (2) vision-language model (2) distribution shift (2) domain adaptation (2) uncertainty quantification (1) transfer learning (1) robust optimization (1) policy optimization (1) few-shot learning (1) reinforcement learning (1)

Papers

RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization ACL 2025 Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing ICML 2025 FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees ICML 2025 MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment ICML 2025 MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models ICLR 2025 Analyzing and Mitigating Object Hallucination in Large Vision-Language Models ICLR 2024 RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models EMNLP 2024 Calibrated Self-Rewarding Vision Language Models NIPS 2024 Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks ICML 2024 Conformal Prediction for Deep Classifier via Label Ranking ICML 2024 S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity NIPS 2024 Order-Independence Without Fine Tuning NIPS 2024 The Power of Contrast for Feature Learning: A Theoretical Analysis JMLR 2023 Beyond Confidence: Reliable Models Should Also Consider Atypicality NIPS 2023 Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data AISTATS 2023 Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise AISTATS 2023 Reinforcement Learning with Stepwise Fairness Constraints AISTATS 2023 FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data ICLR 2023 FaiREE: fair classification with finite-sample and distribution-free guarantee ICLR 2023 Discover and Cure: Concept-aware Mitigation of Spurious Correlation ICML 2023 When and How Mixup Improves Calibration ICML 2022 Improving Out-of-Distribution Robustness via Selective Augmentation ICML 2022 C-Mixup: Improving Generalization in Regression NIPS 2022 Meta-Learning with Fewer Tasks through Task Interpolation ICLR 2022 Adversarial Training Helps Transfer Learning via Better Representations NIPS 2021 Improving Generalization in Meta-learning via Task Augmentation ICML 2021 A Central Limit Theorem for Differentially Private Query Answering NIPS 2021 How Does Mixup Help With Robustness and Generalization? ICLR 2021 Improving Adversarial Robustness via Unlabeled Out-of-Domain Data AISTATS 2021 Interpreting Robust Optimization via Adversarial Influence Functions ICML 2020