A Strategy of Merging Branches Based on Margin Enlargement of SVM in Decision Tree Induction. - Chenxiao Yang; Xizhao Wang; Ruixian Zhu (SMC, 2006)

Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders. - Qitian Wu; Chenxiao Yang; Shuodian Yu; Xiaofeng Gao 001; Guihai Chen (CIKM, 2021)

MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation. - Chenxiao Yang; Jiale Zhang; Xiaofeng Gao 001; Guihai Chen (ICWS, 2021)

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. - Qitian Wu; Chenxiao Yang; Junchi Yan (NeurIPS, 2021)

Cross-Task Knowledge Distillation in Multi-Task Recommendation. - Chenxiao Yang; Junwei Pan; Xiaofeng Gao 001; Tingyu Jiang; Dapeng Liu; Guihai Chen (AAAI, 2022)

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach. - Chenxiao Yang; Qitian Wu; Jipeng Jin; Xiaofeng Gao 001; Junwei Pan; Guihai Chen (IJCAI, 2022)

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. - Chenxiao Yang; Qitian Wu; Qingsong Wen; Zhiqiang Zhou; Liang Sun 001; Junchi Yan (NeurIPS, 2022)

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. - Chenxiao Yang; Qitian Wu; Junchi Yan (NeurIPS, 2022)

A Chaotic Image Encryption Scheme Based on Genetic Central Dogma and KMP Method. - Yuwen Sha; Sun Bo; Chenxiao Yang; Jun Mou; Hadi Jahanshahi (Int. J. Bifurc. Chaos, 2022)

Energy-based Out-of-Distribution Detection for Graph Neural Networks. - Qitian Wu; Yiting Chen 003; Chenxiao Yang; Junchi Yan (ICLR, 2023)

DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. - Qitian Wu; Chenxiao Yang; Wentao Zhao; Yixuan He; David Wipf; Junchi Yan (ICLR, 2023)

Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. - Chenxiao Yang; Qitian Wu; Jiahua Wang; Junchi Yan (ICLR, 2023)

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. - Wentao Zhao; Qitian Wu; Chenxiao Yang; Junchi Yan (KDD, 2023)

Simplifying and Empowering Transformers for Large-Graph Representations. - Qitian Wu; Wentao Zhao; Chenxiao Yang; Hengrui Zhang; Fan Nie; Haitian Jiang; Yatao Bian; Junchi Yan (NeurIPS, 2023)

How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space. - Chenxiao Yang; Qitian Wu; David Wipf; Ruoyu Sun 001; Junchi Yan (CoRR, 2023)

Advective Diffusion Transformers for Topological Generalization in Graph Learning. - Qitian Wu; Chenxiao Yang; Kaipeng Zeng; Fan Nie; Michael M. Bronstein; Junchi Yan (CoRR, 2023)

InfoMLP: Unlocking the Potential of MLPs for Semi-Supervised Learning with Structured Data. - Hengrui Zhang; Qitian Wu; Chenxiao Yang; Philip S. Yu (CIKM, 2024)

Learning Divergence Fields for Shift-Robust Graph Representations. - Qitian Wu; Fan Nie; Chenxiao Yang; Junchi Yan (ICML, 2024)

How Graph Neural Networks Learn: Lessons from Training Dynamics. - Chenxiao Yang; Qitian Wu; David Wipf; Ruoyu Sun 001; Junchi Yan (ICML, 2024)

GeoMix: Towards Geometry-Aware Data Augmentation. - Wentao Zhao; Qitian Wu; Chenxiao Yang; Junchi Yan (KDD, 2024)

Graph Out-of-Distribution Generalization via Causal Intervention. - Qitian Wu; Fan Nie; Chenxiao Yang; Tianyi Bao; Junchi Yan (WWW, 2024)

Chain-of-Thought Provably Enables Learning the (Otherwise) Unlearnable. - Chenxiao Yang; Zhiyuan Li 005; David Wipf (ICLR, 2025)

PENCIL: Long Thoughts with Short Memory. - Chenxiao Yang; Nathan Srebro; David McAllester; Zhiyuan Li 005 (CoRR, 2025)