Wenbo Hu's Homepage – Publications

Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series.
Ying Liu+, Peng Cui+, Wenbo Hu, Richang Hong
[arXiv]

Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning.
Youze Wang, Wenbo Hu, Yinpeng Dong, Hanwang Zhang, Richang Hong.
[arXiv]

Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation.
Wenbo Hu+, Xin Sun+, Qiang liu, Shu Wu.
[arXiv]

Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui, Zhijie Deng, Wenbo Hu and Jun Zhu.
[arXiv]

A dilated convolution based method with time series fine tuning for data-driven crack length estimation.
Jiaxin Gao+, Wenbo Hu+, Qinan Han, Yuntian Chen, Richang Hong, Huiji Shi
Fatigue & Fracture of Engineering Materials & Structures
[paper]

Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting.
Guande He, Peng Cui, Jianfei Chen, Wenbo Hu, Jun Zhu
ICLR 2024 Workshop ME-FoMo
[arXiv]

How to Use Language Expert to Assist Inference for Visual Commonsense Reasoning.
Zijie Song+, Wenbo Hu+, Hao Ye, and Richang Hong.
ICDM 2023 International Workshop on Learning with Knowledge Graphs
[paper]

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting.
Jiaxin Gao, Wenbo Hu, Yuntian Chen.
KDD 2023 Workshop on Mining and Learning from Time Series (KDD’23-MiLeTS)
[arXiv] [Code]

Iterative Adversarial Attack on Image-guided Story Ending Generation.
Youze Wang, Wenbo Hu, Richang Hong.
IEEE Transactions on Multimedia
[paper] [arXiv] [Code]

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations .
Yingtao Luo, Qiang Liu, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu.
KDD 2023.
[paper] [arXiv]
A preliminary version presented in NeurIPS 2022 Workshop AI4Science.

An Adaptive Deep-learning Load Forecasting Framework by Integrating Transformer and Domain Knowledge.
Jiaxin Gao, Yuntian Chen, Wenbo Hu, Dongxiao Zhang.
Advances in Applied Energy
[paper] [Code]

Stacking VAE with Graph Neural Networks for Effective and Interpretable Time Series Anomaly Detection.
Wenkai Li, Wenbo Hu, Ting Chen, Ning Chen, Cheng Feng. AI Open, 2022.
[paper] [arXiv] [Code]

TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning.
Jiaxin Gao, Wenbo Hu, Dongxiao Zhang, Yuntian Chen.
The 14th International Conference on Applied Energy (ICAE), 2022.
[arXiv]

Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting
Qingyi Pan, Wenbo Hu and Ning Chen. The 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021.
[paper] [pdf]

Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui, Wenbo Hu and Jun Zhu. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020.
[paper] [arXiv] [pdf]

Dynamic Window-level Granger Causality of Multi-channel Time Series
Zhiheng Zhang, Wenbo Hu and Jun Zhu.
[arXiv]

大坝监测数据的时序预测与补全. (Time Series Forecasting and Imputation of Dam Physical Quantities)
Manling Du, Jiaxin Gao, Libing Zhang, Mingqing Luo, Yuntian Chen, Wenbo Hu, Tian Tian. 水力发电杂志. (Water Power Journal), 2020.
[pdf]

SAM: Semantic Attribute Modulated Language Modeling
Wenbo Hu, Lifeng Hua, Lei Li, Hang Su, Tian Wang, Ning Chen and Bo Zhang.
Eighth Cross-Strait Tsinghua Postgraduate Academic Forum 2018, won the Best Paper Award.
[arXiv]

基于图的半监督学习理论与应用 (Graph-based Semi-supervised Learning:Theory and Applications)
Hang Su, Yinpeng Dong, Wenbo Hu. 中国人工智能学会通讯 (CAAI Transactions on Intelligence Technology), 2017年第12期.
[pdf]

Semi-supervised Max-margin Topic Models with Manifold Posterior Regularization
Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, and Bo Zhang. International Joint Conference on Artificial Intelligence (IJCAI), 2017.
[paper] [pdf]

Big learning with Bayesian Methods
Jun Zhu, Jianfei Chen, Wenbo Hu and Bo Zhang. National Science Review (NSR), 2017.
[paper] [arXiv] [pdf]

Fast Sampling for Bayesian Max-Margin Models
Wenbo Hu, Jun Zhu and Bo Zhang. Expert Systems with Applications (ESWA), 2017.
An early version was published on AEARU-CSWT 2015, won the Best Poster Award.
[paper] [arXiv] [pdf]

贝叶斯机器学习前沿进展综述 (Recent Advances in Bayesian Machine Learning)
Jun Zhu and Wenbo Hu. 计算机研究与发展(Journal of Computer Science and Development), 2015. (In Chinese)
Influential as listed on most ten downloaded paper of the journal. [Check here.]
[paper] [pdf]