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Linjun Zhang

31 papers · 2020–2026 · 7 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

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+11 more ↓ πŸ—ΊοΈ Taxonomy Completionist (64) πŸƒ Academic Marathon Β· Sprint 🌍 Conference Polyglot Β· Silver πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
🀝 Dynamic Duo (13) 🧬 Topic Evolution πŸ† Keyword Champion πŸ‘‘ Triple Crown 🌈 Renaissance Researcher (7) πŸ”₯ Unstoppable Β· Bronze ❓ The Questioner ⚑ Prolific Year Β· Productive πŸ’Ž Century Club Β· Bronze πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (146)

Venues

ICML (10) NeurIPS (7) ICLR (6) AISTATS (4) JMLR (2) ACL (1) EMNLP (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

Machine Learning (28) Artificial Intelligence (17) Learning Types Β· Machine Learning (17) Core AI (15) Application Areas (14) Optimization & Theory (11) Core Methods (9) Deep Learning (7) Natural Language Processing (6) Learning Types Β· Deep Learning (5) Learning Paradigms (4) Applications (3) Models (3) Reinforcement Learning (2) Generation (2)

Keywords

data augmentation (4) representation learning (4) domain generalization (3) semi-supervised learning (3) contrastive learning (3) retrieval-augmented generation (3) large language model (3) multimodal learning (3) vision-language model (2) equalized odd (2) differential privacy (2) group fairness (2) fair classification (2) distribution shift (2) adversarial robustness (2) domain adaptation (2) adversarial training (2) preference optimization (2) few-shot learning (2) spurious correlation (2)

Papers

Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control JMLR 2026 FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees ICML 2025 Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing ICML 2025 MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models ICLR 2025 MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment ICML 2025 RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization ACL 2025 Analyzing and Mitigating Object Hallucination in Large Vision-Language Models ICLR 2024 Calibrated Self-Rewarding Vision Language Models NeurIPS 2024 Conformal Prediction for Deep Classifier via Label Ranking ICML 2024 Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks ICML 2024 Order-Independence Without Fine Tuning NeurIPS 2024 RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models EMNLP 2024 S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity NeurIPS 2024 Beyond Confidence: Reliable Models Should Also Consider Atypicality NeurIPS 2023 Discover and Cure: Concept-aware Mitigation of Spurious Correlation ICML 2023 FaiREE: fair classification with finite-sample and distribution-free guarantee ICLR 2023 FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data ICLR 2023 Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise AISTATS 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 C-Mixup: Improving Generalization in Regression NeurIPS 2022 Improving Out-of-Distribution Robustness via Selective Augmentation ICML 2022 Meta-Learning with Fewer Tasks through Task Interpolation ICLR 2022 When and How Mixup Improves Calibration ICML 2022 A Central Limit Theorem for Differentially Private Query Answering NeurIPS 2021 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 Improving Generalization in Meta-learning via Task Augmentation ICML 2021 Interpreting Robust Optimization via Adversarial Influence Functions ICML 2020