山东大学教师主页孙皓亮 Paper Publications Home

Sun Haoliang Associate Professor

山东省高层次青年人才(泰山学者青年专家)，现任副研究员，硕士生导师。本博毕业于山东大学，博士期间赴加拿大、美国联合培养，曾在阿联酋起源人工智能研究院实习。研究方向概率机器学习及受限条件下的鲁棒机器学习算法，即研究利用概率模型建模机器学习中的不确定性，推断出多种可能性及相应概率分布，以达到鲁棒可信学习目标；近期关注人机互补学习(Learning to Defer)、大模型、具身智能。已在顶级期刊和会议上发表论文20余篇，包括IEEE TPAMI、IJCV、ML... More+

Paper Publications

Home >> Scientific Research >> Paper Publications

[1] 李方凯. Cross-graph meta matching correction for noisy graph matching. COMPUTER VISION AND IMAGE UNDERSTANDING, 259, 2025.
[2] 王任. SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning. Proceedings of the Computer Vision and Pattern Recognition Conference, 2025.
[3] 牟文芊. GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation. NEURAL NETWORKS, 2025.
[4] 余飘. Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution. Expert Systems with Applications, 2024.
[5] 安洋. Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction. TKDE, 2025.
[6] Haoliang Sun et al.. Variational Rectification Inference for Learning with Noisy Labels. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2024.
[7] 杜英军等 and 孙皓亮. MetaKernel: Learning Variational Random Features with Limited Labels. T-PAMI, 2024.
[8] 魏琦等. Learning Sample-Aware Threshold for Semi-Supervised Learning. MACHINE LEARNING, 113, 5423, 2024.
[9] 王任等. MetaViewer: Towards A Unified Multi-View Representation. 《IEEE Conference on Computer Vision and Pattern Recognition》, 2023.
[10] 魏琦等. Fine-Grained Classification with Noisy Labels. 《IEEE Conference on Computer Vision and Pattern Recognition》, 2023.
[11] 孙皓亮等. Attentional Prototype Inference for Few-Shot Segmentation. PR, 2023.
[12] 孙皓亮等. Learning to Rectify for Robust Learning with Noisy Labels. PR, 2022.
[13] 魏琦等 and 孙皓亮. Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization. ECCV, 2022.
[14] 甄先通、孙皓亮等. Learning to Learn Kernels with Variational Random Features. ICML, 2020.
[15] 孙皓亮等. DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer. ICCV, 2019.
[16] 孙皓亮等. Learning Deep Match Kernels for Image-Set Classification. CVPR, 2017.

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Paper Publications

[1] 李方凯. Cross-graph meta matching correction for noisy graph matching. COMPUTER VISION AND IMAGE UNDERSTANDING, 259, 2025.

[2] 王任. SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning. Proceedings of the Computer Vision and Pattern Recognition Conference, 2025.

[3] 牟文芊. GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation. NEURAL NETWORKS, 2025.

[4] 余飘. Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution. Expert Systems with Applications, 2024.

[5] 安洋. Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction. TKDE, 2025.

[6] Haoliang Sun et al.. Variational Rectification Inference for Learning with Noisy Labels. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2024.

[7] 杜英军等 and 孙皓亮. MetaKernel: Learning Variational Random Features with Limited Labels. T-PAMI, 2024.

[8] 魏琦等. Learning Sample-Aware Threshold for Semi-Supervised Learning. MACHINE LEARNING, 113, 5423, 2024.

[9] 王任等. MetaViewer: Towards A Unified Multi-View Representation. 《IEEE Conference on Computer Vision and Pattern Recognition》, 2023.

[10] 魏琦等. Fine-Grained Classification with Noisy Labels. 《IEEE Conference on Computer Vision and Pattern Recognition》, 2023.

[11] 孙皓亮等. Attentional Prototype Inference for Few-Shot Segmentation. PR, 2023.

[12] 孙皓亮等. Learning to Rectify for Robust Learning with Noisy Labels. PR, 2022.

[13] 魏琦等 and 孙皓亮. Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization. ECCV, 2022.

[14] 甄先通、孙皓亮等. Learning to Learn Kernels with Variational Random Features. ICML, 2020.

[15] 孙皓亮等. DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer. ICCV, 2019.

[16] 孙皓亮等. Learning Deep Match Kernels for Image-Set Classification. CVPR, 2017.