Object Detection Based on Deep Feature for Optical Remote Sensing Images. - Xujiang Zhao; Shouhong Wan; Chang Zou; Xingyue Li; Li Yan (GSKI (1), 2017)

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data. - Xujiang Zhao; Feng Chen 001; Jin-Hee Cho (IEEE BigData, 2018)

Deep Learning Based Scalable Inference of Uncertain Opinions. - Xujiang Zhao; Feng Chen 001; Jin-Hee Cho (ICDM, 2018)

Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks. - Xujiang Zhao; Feng Chen 001; Jin-Hee Cho (MILCOM, 2018)

Uncertainty-Aware Opinion Inference Under Adversarial Attacks. - Adil Alim; Xujiang Zhao; Jin-Hee Cho; Feng Chen 001 (IEEE BigData, 2019)

Uncertainty-based Decision Making Using Deep Reinforcement Learning. - Xujiang Zhao; Shu Hu; Jin-Hee Cho; Feng Chen 001 (FUSION, 2019)

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks. - Xujiang Zhao; Yuzhe Ou; Lance M. Kaplan; Feng Chen 001; Jin-Hee Cho (CoRR, 2019)

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. - Weishi Shi; Xujiang Zhao; Feng Chen 001; Qi Yu 001 (NeurIPS, 2020)

Uncertainty Aware Semi-Supervised Learning on Graph Data. - Xujiang Zhao; Feng Chen 001; Shu Hu; Jin-Hee Cho (NeurIPS, 2020)

Robust Semi-Supervised Learning with Out of Distribution Data. - Xujiang Zhao; KrishnaTeja Killamsetty; Rishabh K. Iyer; Feng Chen 001 (CoRR, 2020)

Multidimensional Uncertainty-Aware Evidential Neural Networks. - Yibo Hu 002; Yuzhe Ou; Xujiang Zhao; Jin-Hee Cho; Feng Chen 001 (AAAI, 2021)

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation. - Liyan Xu; Xuchao Zhang; Xujiang Zhao; Haifeng Chen; Feng Chen 001; Jinho D. Choi (EMNLP (1), 2021)

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning. - KrishnaTeja Killamsetty; Xujiang Zhao; Feng Chen 001; Rishabh K. Iyer (NeurIPS, 2021)

CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining. - Zhuoyi Wang; Yuqiao Chen; Chen Zhao 010; Yu Lin 002; Xujiang Zhao; Hemeng Tao; Yigong Wang; Latifur Khan (WWW, 2021)

Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty. - Xueying Yang; Jiamian Wang; Xujiang Zhao; Sheng Li 001; Zhiqiang Tao (CIKM, 2022)

Seed: Sound Event Early Detection Via Evidential Uncertainty. - Xujiang Zhao; Xuchao Zhang; Wei Cheng 002; Wenchao Yu; Yuncong Chen; Haifeng Chen; Feng Chen 001 (ICASSP, 2022)

How Out-of-Distribution Data Hurts Semi-Supervised Learning. - Xujiang Zhao; KrishnaTeja Killamsetty; Rishabh K. Iyer; Feng Chen 001 (ICDM, 2022)

Layer Adaptive Deep Neural Networks for Out-of-Distribution Detection. - Haoliang Wang; Chen Zhao 010; Xujiang Zhao; Feng Chen 001 (PAKDD (2), 2022)

A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning. - Zhen Guo; Zelin Wan; Qisheng Zhang; Xujiang Zhao; Feng Chen 001; Jin-Hee Cho; Qi Zhang; Lance M. Kaplan; Dong Hyun Jeong; Audun Jøsang (CoRR, 2022)

Adaptation Speed Analysis for Fairness-aware Causal Models. - Yujie Lin; Chen Zhao 010; Minglai Shao 001; Xujiang Zhao; Haifeng Chen (CIKM, 2023)

Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning. - Weili Shi; Xueying Yang; Xujiang Zhao; Haifeng Chen; Zhiqiang Tao; Sheng Li 001 (CIKM, 2023)

Open-ended Commonsense Reasoning with Unrestricted Answer Candidates. - Chen Ling 003; Xuchao Zhang; Xujiang Zhao; Yanchi Liu; Wei Cheng 002; Mika Oishi; Takao Osaki; Katsushi Matsuda; Haifeng Chen; Liang Zhao 002 (EMNLP (Findings), 2023)

Multi-Label Temporal Evidential Neural Networks for Early Event Detection. - Xujiang Zhao; Xuchao Zhang; Chen Zhao 010; Jin-Hee Cho; Lance M. Kaplan; Dong Hyun Jeong; Audun Jøsang; Haifeng Chen; Feng Chen 001 (ICASSP, 2023)

2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making. - Xujiang Zhao; Chen Zhao 010; Feng Chen 001; Jin-Hee Cho; Haifeng Chen (KDD, 2023)

Knowledge-enhanced Neural Machine Reasoning: A Review. - Tanmoy Chowdhury; Chen Ling 003; Xuchao Zhang; Xujiang Zhao; Guangji Bai; Jian Pei 001; Haifeng Chen; Liang Zhao 002 (CoRR, 2023)

Dynamic Prompting: A Unified Framework for Prompt Tuning. - Xianjun Yang; Wei Cheng 002; Xujiang Zhao; Linda R. Petzold; Haifeng Chen (CoRR, 2023)

Multidimensional Uncertainty Quantification for Deep Neural Networks. - Xujiang Zhao (CoRR, 2023)

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models. - Chen Ling 003; Xujiang Zhao; Jiaying Lu 001; Chengyuan Deng; Can Zheng; Junxiang Wang; Tanmoy Chowdhury; Yun Li 005; Hejie Cui; Xuchao Zhang; Tianjiao Zhao; Amit Panalkar; et al. (CoRR, 2023)

Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty. - Chen Ling 003; Xujiang Zhao; Xuchao Zhang; Yanchi Liu; Wei Cheng 002; Haoyu Wang; Zhengzhang Chen; Takao Osaki; Katsushi Matsuda; Haifeng Chen; Liang Zhao 002 (CoRR, 2023)

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains. - Yujie Lin; Chen Zhao 010; Minglai Shao 001; Baoluo Meng; Xujiang Zhao; Haifeng Chen (CoRR, 2023)

Large Language Models Can Be Good Privacy Protection Learners. - Yijia Xiao; Yiqiao Jin; Yushi Bai; Yue Wu; Xianjun Yang; Xiao Luo 001; Wenchao Yu; Xujiang Zhao; Yanchi Liu; Haifeng Chen; Wei Wang 010; Wei Cheng 002 (CoRR, 2023)

Open-ended Commonsense Reasoning with Unrestricted Answer Scope. - Chen Ling 003; Xuchao Zhang; Xujiang Zhao; Yanchi Liu; Wei Cheng 002; Mika Oishi; Takao Osaki; Katsushi Matsuda; Haifeng Chen; Liang Zhao 002 (CoRR, 2023)

Large Language Models Can Be Contextual Privacy Protection Learners. - Yijia Xiao; Yiqiao Jin; Yushi Bai; Yue Wu; Xianjun Yang; Xiao Luo 001; Wenchao Yu; Xujiang Zhao; Yanchi Liu; Quanquan Gu; Haifeng Chen; Wei Wang 010; et al. (EMNLP, 2024)

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments. - Yujie Lin; Chen Zhao 010; Minglai Shao 001; Baoluo Meng; Xujiang Zhao; Haifeng Chen (IJCAI, 2024)

3rd Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM). - Xujiang Zhao; Chen Zhao 010; Feng Chen 001; Jin-Hee Cho; Wei Hua; Haifeng Chen (KDD, 2024)

Uncertainty Quantification for In-Context Learning of Large Language Models. - Chen Ling 003; Xujiang Zhao; Xuchao Zhang; Wei Cheng 002; Yanchi Liu; Yiyou Sun; Mika Oishi; Takao Osaki; Katsushi Matsuda; Jie Ji; Guangji Bai; Liang Zhao 002; et al. (NAACL-HLT, 2024)

Pruning as a Domain-specific LLM Extractor. - Nan Zhang; Yanchi Liu; Xujiang Zhao; Wei Cheng 002; Runxue Bao; Rui Zhang 037; Prasenjit Mitra; Haifeng Chen (NAACL-HLT (Findings), 2024)

Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models. - Chen Ling 003; Xujiang Zhao; Wei Cheng 002; Yanchi Liu; Yiyou Sun; Xuchao Zhang; Mika Oishi; Takao Osaki; Katsushi Matsuda; Jie Ji; Guangji Bai; Liang Zhao 002; et al. (CoRR, 2024)

RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection. - Chengyuan Deng; Zhengzhang Chen; Xujiang Zhao; Haoyu Wang; Junxiang Wang; Haifeng Chen; Jie Gao (CoRR, 2024)

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation. - Minhua Lin; Zhengzhang Chen; Yanchi Liu; Xujiang Zhao; Zongyu Wu 001; Junxiang Wang; Xiang Zhang 001; Suhang Wang; Haifeng Chen (CoRR, 2024)

Scattered Forest Search: Smarter Code Space Exploration with LLMs. - Jonathan Light; Yue Wu; Yiyou Sun; Wenchao Yu; Yanchi Liu; Xujiang Zhao; Ziniu Hu; Haifeng Chen; Wei Cheng 002 (CoRR, 2024)

SAUP: Situation Awareness Uncertainty Propagation on LLM Agent. - Qiwei Zhao; Xujiang Zhao; Yanchi Liu; Wei Cheng 002; Yiyou Sun; Mika Oishi; Takao Osaki; Katsushi Matsuda; Huaxiu Yao; Haifeng Chen (CoRR, 2024)

Quantifying uncertainty in graph neural network explanations. - Junji Jiang; Chen Ling 003; Hongyi Li; Guangji Bai; Xujiang Zhao; Liang Zhao 002 (Frontiers Big Data, 2024)

A survey on uncertainty reasoning and quantification in belief theory and its application to deep learning. - Zhen Guo; Zelin Wan; Qisheng Zhang; Xujiang Zhao; Qi Zhang; Lance M. Kaplan; Audun Jøsang; Dong Hyun Jeong; Feng Chen 001; Jin-Hee Cho (Inf. Fusion, 2024)

Uncertainty Propagation on LLM Agent. - Qiwei Zhao; Dong Li; Yanchi Liu; Wei Cheng 002; Yiyou Sun; Mika Oishi; Takao Osaki; Katsushi Matsuda; Huaxiu Yao; Chen Zhao; Haifeng Chen; Xujiang Zhao (ACL (1), 2025)

SFS: Smarter Code Space Search improves LLM Inference Scaling. - Jonathan Light; Yue Wu; Yiyou Sun; Wenchao Yu; Yanchi Liu; Xujiang Zhao; Ziniu Hu; Haifeng Chen; Wei Cheng 002 (ICLR, 2025)

MixLLM: Dynamic Routing in Mixed Large Language Models. - Xinyuan Wang 011; Yanchi Liu; Wei Cheng 002; Xujiang Zhao; Zhengzhang Chen; Wenchao Yu; Yanjie Fu; Haifeng Chen (NAACL (Long Papers), 2025)

Position Really Matters: Towards a Holistic Approach for Prompt Tuning. - Xianjun Yang; Wei Cheng 002; Xujiang Zhao; Wenchao Yu; Linda Ruth Petzold; Haifeng Chen (NAACL (Findings), 2025)

Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs. - Ruomeng Ding; Xujiang Zhao; Chen Zhao 010; Minglai Shao 001; Zhengzhang Chen; Haifeng Chen (SDM, 2025)

DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router. - Minghao Guo; Qingcheng Zeng; Xujiang Zhao; Yanchi Liu; Wenchao Yu; Mengnan Du; Haifeng Chen; Wei Cheng 002 (CoRR, 2025)