Xujiang Zhao

According to our database, Xujiang Zhao authored at least 42 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Jin-Hee Cho

Inf. Fusion, January, 2024

Quantifying uncertainty in graph neural network explanations.

Junji Jiang, Chen Ling, Hongyi Li, Guangji Bai, Xujiang Zhao, Liang Zhao

Frontiers Big Data, 2024

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation.

Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen

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

Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models.

Chen Ling, Xujiang Zhao, Wei Cheng, Yanchi Liu, Yiyou Sun, Xuchao Zhang, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

CoRR, 2024

Pruning as a Domain-specific LLM Extractor.

Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Uncertainty Quantification for In-Context Learning of Large Language Models.

Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

3rd Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM).

Xujiang Zhao, Chen Zhao, Feng Chen, Jin-Hee Cho, Wei Hua, Haifeng Chen

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments.

Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Large Language Models Can Be Contextual Privacy Protection Learners.

Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Open-ended Commonsense Reasoning with Unrestricted Answer Scope.

Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

CoRR, 2023

Large Language Models Can Be Good Privacy Protection Learners.

Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng

CoRR, 2023

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains.

Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen

CoRR, 2023

Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty.

Chen Ling, Xujiang Zhao, Xuchao Zhang, Yanchi Liu, Wei Cheng, Haoyu Wang, Zhengzhang Chen, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

CoRR, 2023

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models.

Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Carl Yang, Liang Zhao

CoRR, 2023

Multidimensional Uncertainty Quantification for Deep Neural Networks.

Xujiang Zhao

CoRR, 2023

Dynamic Prompting: A Unified Framework for Prompt Tuning.

Xianjun Yang, Wei Cheng, Xujiang Zhao, Linda R. Petzold, Haifeng Chen

CoRR, 2023

Knowledge-enhanced Neural Machine Reasoning: A Review.

Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao

CoRR, 2023

2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making.

Xujiang Zhao, Chen Zhao, Feng Chen, Jin-Hee Cho, Haifeng Chen

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Multi-Label Temporal Evidential Neural Networks for Early Event Detection.

Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-Hee Cho, Lance M. Kaplan, Dong Hyun Jeong, Audun Jøsang, Haifeng Chen, Feng Chen

Proceedings of the IEEE International Conference on Acoustics, 2023

Open-ended Commonsense Reasoning with Unrestricted Answer Candidates.

Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 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

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Adaptation Speed Analysis for Fairness-aware Causal Models.

Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

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, Jin-Hee Cho, Qi Zhang, Lance M. Kaplan, Dong Hyun Jeong, Audun Jøsang

CoRR, 2022

Layer Adaptive Deep Neural Networks for Out-of-Distribution Detection.

Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

How Out-of-Distribution Data Hurts Semi-Supervised Learning.

Xujiang Zhao, KrishnaTeja Killamsetty, Rishabh K. Iyer, Feng Chen

Proceedings of the IEEE International Conference on Data Mining, 2022

Seed: Sound Event Early Detection Via Evidential Uncertainty.

Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen

Proceedings of the IEEE International Conference on Acoustics, 2022

Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty.

Xueying Yang, Jiamian Wang, Xujiang Zhao, Sheng Li, Zhiqiang Tao

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining.

Zhuoyi Wang, Yuqiao Chen, Chen Zhao, Yu Lin, Xujiang Zhao, Hemeng Tao, Yigong Wang, Latifur Khan

Proceedings of the WWW '21: The Web Conference 2021, 2021

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning.

KrishnaTeja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh K. Iyer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation.

Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Multidimensional Uncertainty-Aware Evidential Neural Networks.

Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, Feng Chen

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Robust Semi-Supervised Learning with Out of Distribution Data.

Xujiang Zhao, KrishnaTeja Killamsetty, Rishabh K. Iyer, Feng Chen

CoRR, 2020

Uncertainty Aware Semi-Supervised Learning on Graph Data.

Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning.

Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks.

Xujiang Zhao, Yuzhe Ou, Lance M. Kaplan, Feng Chen, Jin-Hee Cho

CoRR, 2019

Uncertainty-based Decision Making Using Deep Reinforcement Learning.

Xujiang Zhao, Shu Hu, Jin-Hee Cho, Feng Chen

Proceedings of the 22th International Conference on Information Fusion, 2019

Uncertainty-Aware Opinion Inference Under Adversarial Attacks.

Adil Alim, Xujiang Zhao, Jin-Hee Cho, Feng Chen

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks.

Xujiang Zhao, Feng Chen, Jin-Hee Cho

Proceedings of the 2018 IEEE Military Communications Conference, 2018

Deep Learning Based Scalable Inference of Uncertain Opinions.

Xujiang Zhao, Feng Chen, Jin-Hee Cho

Proceedings of the IEEE International Conference on Data Mining, 2018

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data.

Xujiang Zhao, Feng Chen, Jin-Hee Cho

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Object Detection Based on Deep Feature for Optical Remote Sensing Images.

Xujiang Zhao, Shouhong Wan, Chang Zou, Xingyue Li, Li Yan

Proceedings of the Geo-Spatial Knowledge and Intelligence - 5th International Conference, 2017