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2020 – today
- 2024
[c30]Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao:
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models. EMNLP 2024: 1081-1093- [c29]Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao:
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. ICLR 2024 - [c28]Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei:
Conformal Prediction for Deep Classifier via Label Ranking. ICML 2024 - [c27]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. ICML 2024 - [c26]Zexing Xu, Linjun Zhang, Sitan Yang, Nan Jiang:
Peak Period Demand Forecasting with Proxy Data: GNN-Enhanced Meta-Learning. WACV (Workshops) 2024: 726-735 - [i46]Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J. Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi:
Can AI Be as Creative as Humans? CoRR abs/2401.01623 (2024) - [i45]Xintao Xia, Linjun Zhang, Zhanrui Cai:
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm. CoRR abs/2401.08150 (2024) - [i44]Zongbo Han, Yifeng Yang, Changqing Zhang, Linjun Zhang, Joey Tianyi Zhou, Qinghua Hu, Huaxiu Yao:
Selective Learning: Towards Robust Calibration with Dynamic Regularization. CoRR abs/2402.08384 (2024) - [i43]Qichuan Yin, Junzhou Huang, Huaxiu Yao, Linjun Zhang:
Distribution-Free Fair Federated Learning with Small Samples. CoRR abs/2402.16158 (2024) - [i42]Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang:
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. CoRR abs/2403.05006 (2024) - [i41]Tianxi Cai, Feiqing Huang, Ryumei Nakada, Linjun Zhang, Doudou Zhou:
Contrastive Learning on Multimodal Analysis of Electronic Health Records. CoRR abs/2403.14926 (2024) - [i40]Sai Li, Linjun Zhang:
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality. CoRR abs/2404.01608 (2024) - [i39]Zhe Zhang, Ryumei Nakada, Linjun Zhang:
Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference. CoRR abs/2404.16287 (2024) - [i38]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. CoRR abs/2405.02225 (2024) - [i37]Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng, Sihan Yang, Zhaorun Chen, Chenhang Cui, Xiyao Wang, Yun Li, Linjun Zhang, Huaxiu Yao:
Calibrated Self-Rewarding Vision Language Models. CoRR abs/2405.14622 (2024) - [i36]Ryumei Nakada, Yichen Xu, Lexin Li, Linjun Zhang:
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance. CoRR abs/2406.03628 (2024) - [i35]Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang, Cynthia Dwork:
Set-Based Prompting: Provably Solving the Language Model Order Dependency Problem. CoRR abs/2406.06581 (2024) - [i34]Zexing Xu, Linjun Zhang, Sitan Yang, S. Rasoul Etesami, Hanghang Tong, Huan Zhang, Jiawei Han:
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data. CoRR abs/2406.16221 (2024) - [i33]Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao:
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models. CoRR abs/2407.05131 (2024) - [i32]Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, Haoyu Wang:
NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models. CoRR abs/2410.01870 (2024) - 2023
- [j9]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. J. Mach. Learn. Res. 24: 330:1-330:78 (2023) - [c25]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. AISTATS 2023: 4348-4380 - [c24]Haotian Ye, James Zou, Linjun Zhang:
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. AISTATS 2023: 8968-8990 - [c23]Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. AISTATS 2023: 10594-10618 - [c22]Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou:
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. ICLR 2023 - [c21]Puheng Li, James Zou, Linjun Zhang:
FaiREE: fair classification with finite-sample and distribution-free guarantee. ICLR 2023 - [c20]Shirley Wu, Mert Yüksekgönül, Linjun Zhang, James Zou:
Discover and Cure: Concept-aware Mitigation of Spurious Correlation. ICML 2023: 37765-37786 - [c19]Zhun Deng, Cynthia Dwork, Linjun Zhang
:
HappyMap : A Generalized Multicalibration Method. ITCS 2023: 41:1-41:23 - [c18]Mert Yüksekgönül, Linjun Zhang, James Y. Zou, Carlos Guestrin:
Beyond Confidence: Reliable Models Should Also Consider Atypicality. NeurIPS 2023 - [i31]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. CoRR abs/2302.06232 (2023) - [i30]Zhun Deng, Cynthia Dwork, Linjun Zhang:
HappyMap: A Generalized Multi-calibration Method. CoRR abs/2303.04379 (2023) - [i29]T. Tony Cai, Yichen Wang, Linjun Zhang:
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning. CoRR abs/2303.07152 (2023) - [i28]Shirley Wu, Mert Yüksekgönül, Linjun Zhang, James Zou:
Discover and Cure: Concept-aware Mitigation of Spurious Correlation. CoRR abs/2305.00650 (2023) - [i27]Mert Yüksekgönül, Linjun Zhang, James Zou, Carlos Guestrin:
Beyond Confidence: Reliable Models Should Also Consider Atypicality. CoRR abs/2305.18262 (2023) - [i26]Alyssa Huang, Peihan Liu, Ryumei Nakada, Linjun Zhang, Wanrong Zhang:
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training. CoRR abs/2306.08173 (2023) - [i25]Xinming Tu, James Zou, Weijie J. Su, Linjun Zhang:
What Should Data Science Education Do with Large Language Models? CoRR abs/2307.02792 (2023) - [i24]Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao:
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. CoRR abs/2310.00754 (2023) - [i23]Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei:
Conformal Prediction for Deep Classifier via Label Ranking. CoRR abs/2310.06430 (2023) - [i22]Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, Huaxiu Yao:
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges. CoRR abs/2311.03287 (2023) - 2022
- [j8]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. Neural Comput. 34(4): 991-1018 (2022) - [j7]Junfeng Lin, Linjun Zhang, Runhua Guo, Saiyi Jiao, Xiaomeng Song, Suting Feng, Ke Wang, Mingyang Li, Yudan Luo, Zaizhu Han:
The influence of visual deprivation on the development of the thalamocortical network: Evidence from congenitally blind children and adults. NeuroImage 264: 119722 (2022) - [c17]Huaxiu Yao, Linjun Zhang, Chelsea Finn:
Meta-Learning with Fewer Tasks through Task Interpolation. ICLR 2022 - [c16]Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn:
Improving Out-of-Distribution Robustness via Selective Augmentation. ICML 2022: 25407-25437 - [c15]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. ICML 2022: 26135-26160 - [c14]Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y. Zou, Chelsea Finn:
C-Mixup: Improving Generalization in Regression. NeurIPS 2022 - [i21]Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn:
Improving Out-of-Distribution Robustness via Selective Augmentation. CoRR abs/2201.00299 (2022) - [i20]Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou:
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. CoRR abs/2206.02792 (2022) - [i19]Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou, Chelsea Finn:
C-Mixup: Improving Generalization in Regression. CoRR abs/2210.05775 (2022) - [i18]Haotian Ye, James Zou, Linjun Zhang:
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. CoRR abs/2210.11075 (2022) - [i17]Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. CoRR abs/2211.03994 (2022) - [i16]Puheng Li, James Zou, Linjun Zhang:
FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee. CoRR abs/2211.15072 (2022) - 2021
- [c13]Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou:
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. AISTATS 2021: 2845-2853 - [c12]Sanghoon Oh, Linjun Zhang, H. Eric Tseng, Lu Xu, Gábor Orosz:
G2 smooth, curvature constrained, local motion planning for automated vehicles. CCTA 2021: 265-270 - [c11]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou:
How Does Mixup Help With Robustness and Generalization? ICLR 2021 - [c10]Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li:
Improving Generalization in Meta-learning via Task Augmentation. ICML 2021: 11887-11897 - [c9]Jinshuo Dong, Weijie J. Su, Linjun Zhang:
A Central Limit Theorem for Differentially Private Query Answering. NeurIPS 2021: 14759-14770 - [c8]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y. Zou:
Adversarial Training Helps Transfer Learning via Better Representations. NeurIPS 2021: 25179-25191 - [i15]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. CoRR abs/2102.06289 (2021) - [i14]Jinshuo Dong, Weijie J. Su, Linjun Zhang:
A Central Limit Theorem for Differentially Private Query Answering. CoRR abs/2103.08721 (2021) - [i13]Zhe Zhang, Linjun Zhang:
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees. CoRR abs/2104.00245 (2021) - [i12]Huaxiu Yao, Linjun Zhang, Chelsea Finn:
Meta-Learning with Fewer Tasks through Task Interpolation. CoRR abs/2106.02695 (2021) - [i11]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou:
Adversarial Training Helps Transfer Learning via Better Representations. CoRR abs/2106.10189 (2021) - [i10]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. CoRR abs/2106.14836 (2021) - [i9]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. CoRR abs/2110.02473 (2021) - [i8]Maya Burhanpurkar, Zhun Deng, Cynthia Dwork, Linjun Zhang:
Scaffolding Sets. CoRR abs/2111.03135 (2021) - 2020
- [j6]Qiang Lin, Zhengxing Man, Yongchun Cao, Tao Deng, Chengcheng Han, Chuangui Cao, Linjun Zhang, Sitao Zeng, Ruiting Gao, Weilan Wang, Jinshui Ji, Xiaodi Huang:
Classifying functional nuclear images with convolutional neural networks: a survey. IET Image Process. 14(14): 3300-3313 (2020) - [c7]Linjun Zhang, Eric H. Tseng:
Motion Prediction of Human-Driven Vehicles in Mixed Traffic with Connected Autonomous Vehicles. ACC 2020: 398-403 - [c6]Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang:
Interpreting Robust Optimization via Adversarial Influence Functions. ICML 2020: 2464-2473 - [i7]Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Y. Zou:
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. CoRR abs/2006.08476 (2020) - [i6]Jin Cao, Yibo Zhao, Linjun Zhang, Jason Li:
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables. CoRR abs/2009.03358 (2020) - [i5]Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang:
Interpreting Robust Optimization via Adversarial Influence Functions. CoRR abs/2010.01247 (2020) - [i4]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Y. Zou:
How Does Mixup Help With Robustness and Generalization? CoRR abs/2010.04819 (2020) - [i3]T. Tony Cai, Yichen Wang, Linjun Zhang:
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds. CoRR abs/2011.03900 (2020)
2010 – 2019
- 2019
- [j5]Zhonghu Xu, Linjun Zhang, Jinqi Shen, Hao Zhou, Xuefeng Liu, Jiannong Cao, Kai Xing:
MRCS: matrix recovery-based communication-efficient compressive sampling on temporal-spatial data of dynamic-scale sparsity in large-scale environmental IoT networks. EURASIP J. Wirel. Commun. Netw. 2019: 18 (2019) - [i2]T. Tony Cai, Yichen Wang, Linjun Zhang:
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy. CoRR abs/1902.04495 (2019) - 2018
- [j4]Linjun Zhang, Jing Sun, Gábor Orosz:
Hierarchical Design of Connected Cruise Control in the Presence of Information Delays and Uncertain Vehicle Dynamics. IEEE Trans. Control. Syst. Technol. 26(1): 139-150 (2018) - [j3]Linjun Zhang, Gábor Orosz:
Beyond-Line-of-Sight Identification by Using Vehicle-to-Vehicle Communication. IEEE Trans. Intell. Transp. Syst. 19(6): 1962-1972 (2018) - 2016
- [j2]Linjun Zhang, Gábor Orosz:
Motif-Based Design for Connected Vehicle Systems in Presence of Heterogeneous Connectivity Structures and Time Delays. IEEE Trans. Intell. Transp. Syst. 17(6): 1638-1651 (2016) - [c5]Linjun Zhang, Gábor Orosz:
Black-box modeling of connected vehicle networks. ACC 2016: 2421-2426 - 2015
- [c4]Linjun Zhang, Gábor Orosz:
Nonlinear dynamics of connected vehicle systems with communication delays. ACC 2015: 2759-2764 - 2014
- [c3]Michael Small, Kevin Judd, Linjun Zhang:
How is that complex network complex? ISCAS 2014: 1263-1266 - 2013
- [i1]Linjun Zhang, Michael Small, Kevin Judd:
Exactly scale-free scale-free networks. CoRR abs/1309.0961 (2013) - 2012
- [c2]Linjun Zhang, Jinkun Liu:
Nonlinear PDE observer design for a flexible two-link manipulator. ACC 2012: 5336-5341 - [c1]Linjun Zhang, Jinkun Liu:
Optimal trajectory control of flexible two-link manipulator based on PDE model. CDC 2012: 4406-4411
2000 – 2009
- 2008
- [j1]Jingjing Zhao, Hua Shu, Linjun Zhang, Xiaoyi Wang, Qiyong Gong, Ping Li:
Cortical competition during language discrimination. NeuroImage 43(3): 624-633 (2008)
Coauthor Index
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