2026
Building Interpretable, Trustworthy Systems for Neural Signal Decoding
Hua XU
40th Annual AAAI Conference on Artificial Intelligence (AAAI) 2026 Accepted
Undergraduate Consortium Section. This is a research proposal focusing on developing interpretable and trustworthy neural signal decoding systems.
Building Interpretable, Trustworthy Systems for Neural Signal Decoding
Hua XU
40th Annual AAAI Conference on Artificial Intelligence (AAAI) 2026 Accepted
Undergraduate Consortium Section. This is a research proposal focusing on developing interpretable and trustworthy neural signal decoding systems.
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Yang Yang*, Hua XU*, Zhangyi Hu*, Yutao Yue (* equal contribution)
arXiv:2510.19698 2026 Under Review
In this work we try to construct explanable rules for scientific discoveries using LLMs. Specifically, we are developing a unified framework integrating LLMs with probabilistic modeling through four stages: rule generation via LLM, weight learning through logistic regression, iterative refinement, and evaluation, achieving higher rule quality and better rule combination effects.
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Yang Yang*, Hua XU*, Zhangyi Hu*, Yutao Yue (* equal contribution)
arXiv:2510.19698 2026 Under Review
In this work we try to construct explanable rules for scientific discoveries using LLMs. Specifically, we are developing a unified framework integrating LLMs with probabilistic modeling through four stages: rule generation via LLM, weight learning through logistic regression, iterative refinement, and evaluation, achieving higher rule quality and better rule combination effects.
2025
IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
Zhangyi Hu*, Jiemin Wu*, Hua XU*, Mingqian Liao, Ninghui Feng, Bo Gao, Songning Lai, Yutao Yue (* equal contribution)
42nd International Conference on Machine Learning (ICML) 2025 Accepted
Leveraging visual pretrained masked autoencoders to address irregular multivariate time series prediction challenges by converting sparse data into time × channel image-like patches, capturing cross-channel interactions with superior accuracy and strong few-shot performance.
IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
Zhangyi Hu*, Jiemin Wu*, Hua XU*, Mingqian Liao, Ninghui Feng, Bo Gao, Songning Lai, Yutao Yue (* equal contribution)
42nd International Conference on Machine Learning (ICML) 2025 Accepted
Leveraging visual pretrained masked autoencoders to address irregular multivariate time series prediction challenges by converting sparse data into time × channel image-like patches, capturing cross-channel interactions with superior accuracy and strong few-shot performance.
vNeck: Electrical Muscle Stimulation Around the Neck for Eyes-Free Target Acquisition in Virtual Reality
Yuchao ZHUO, Hua XU, Yucheng Liu, Duotun Wang, Jianhao Chen, Jiawei Li, Chutian Jiang, Tristan Camille Braud, Mingming Fan
International Conference on Human-Engaged Computing (ICHEC) 2025 Accepted Best Paper
An innovative VR interaction system using electrical muscle stimulation around the neck for eyes-free target acquisition in virtual reality environments. I was responsible for user-study and data processing part.
vNeck: Electrical Muscle Stimulation Around the Neck for Eyes-Free Target Acquisition in Virtual Reality
Yuchao ZHUO, Hua XU, Yucheng Liu, Duotun Wang, Jianhao Chen, Jiawei Li, Chutian Jiang, Tristan Camille Braud, Mingming Fan
International Conference on Human-Engaged Computing (ICHEC) 2025 Accepted Best Paper
An innovative VR interaction system using electrical muscle stimulation around the neck for eyes-free target acquisition in virtual reality environments. I was responsible for user-study and data processing part.