Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. - Chen Ma 001; Yingxue Zhang 001; Qinglong Wang; Xue Liu 001 (CIKM, 2018)

Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification. - Yingxue Zhang 001; Soumyasundar Pal; Mark Coates; Deniz Üstebay (AAAI, 2019)

Multi-graph Convolution Collaborative Filtering. - Jianing Sun; Yingxue Zhang 001; Chen Ma 001; Mark Coates; Huifeng Guo; Ruiming Tang; Xiuqiang He 001 (ICDM, 2019)

Memory Augmented Graph Neural Networks for Sequential Recommendation. - Chen Ma 001; Liheng Ma; Yingxue Zhang 001; Jianing Sun; Xue Liu 001; Mark Coates (AAAI, 2020)

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems. - Yishi Xu; Yingxue Zhang 001; Wei Guo 006; Huifeng Guo; Ruiming Tang; Mark Coates (CIKM, 2020)

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. - Florence Regol; Soumyasundar Pal; Yingxue Zhang 001; Mark Coates (ICML, 2020)

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. - Chen Ma 001; Liheng Ma; Yingxue Zhang 001; Ruiming Tang; Xue Liu 001; Mark Coates (KDD, 2020)

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. - Jianing Sun; Wei Guo 006; Dengcheng Zhang; Yingxue Zhang 001; Florence Regol; Yaochen Hu 001; Huifeng Guo; Ruiming Tang; Han Yuan; Xiuqiang He 001; Mark Coates (KDD, 2020)

Neighbor Interaction Aware Graph Convolution Networks for Recommendation. - Jianing Sun; Yingxue Zhang 001; Wei Guo 006; Huifeng Guo; Ruiming Tang; Xiuqiang He 001; Chen Ma 001; Mark Coates (SIGIR, 2020)

Non Parametric Graph Learning for Bayesian Graph Neural Networks. - Soumyasundar Pal; Saber Malekmohammadi; Florence Regol; Yingxue Zhang 001; Yishi Xu; Mark Coates (UAI, 2020)

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball. - Chen Ma 001; Liheng Ma; Yingxue Zhang 001; Haolun Wu; Xue Liu 001; Mark Coates (AAAI, 2021)

Detection and Defense of Topological Adversarial Attacks on Graphs. - Yingxue Zhang 001; Florence Regol; Soumyasundar Pal; Sakif Khan; Liheng Ma; Mark Coates (AISTATS, 2021)

Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender Systems. - Kian Ahrabian; Yishi Xu; Yingxue Zhang 001; Jiapeng Wu; Yuening Wang; Mark Coates (CIKM, 2021)

Graph Representation Learning via Adversarial Variational Bayes. - Yunhe Li; Yaochen Hu 001; Yingxue Zhang 001 (CIKM, 2021)

Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems. - Yuening Wang; Yingxue Zhang 001; Mark Coates (CIKM, 2021)

Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction. - Amur Ghose; Vincent Zhang; Yingxue Zhang 001; Dong Li 016; Wulong Liu; Mark Coates (ICCAD, 2021)

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting. - Soumyasundar Pal; Liheng Ma; Yingxue Zhang 001; Mark Coates (ICML, 2021)

Dual Graph enhanced Embedding Neural Network for CTR Prediction. - Wei Guo 006; Rong Su 003; Renhao Tan; Huifeng Guo; Yingxue Zhang 001; Zhirong Liu; Ruiming Tang; Xiuqiang He 001 (KDD, 2021)

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion. - Jiapeng Wu; Yishi Xu; Yingxue Zhang 001; Chen Ma 001; Mark Coates; Jackie Chi Kit Cheung (SIGIR, 2021)

Modeling Scale-free Graphs for Knowledge-aware Recommendation. - Yankai Chen 001; Menglin Yang 001; Yingxue Zhang 001; Mengchen Zhao; Ziqiao Meng; Jian Hao; Irwin King (CoRR, 2021)

Content Filtering Enriched GNN Framework for News Recommendation. - Yong Gao; Huifeng Guo; Dandan Lin; Yingxue Zhang 001; Ruiming Tang; Xiuqiang He 001 (CoRR, 2021)

Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation. - Yankai Chen 001; Yifei Zhang; Yingxue Zhang 001; Huifeng Guo; Jingjie Li; Ruiming Tang; Xiuqiang He 001; Irwin King (CoRR, 2021)

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction. - Fuyuan Lyu; Xing Tang 007; Hong Zhu 003; Huifeng Guo; Yingxue Zhang 001; Ruiming Tang; Xue Liu 001 (CIKM, 2022)

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation. - Haolun Wu; Chen Ma 001; Yingxue Zhang 001; Xue Liu 001; Ruiming Tang; Mark Coates (CIKM, 2022)

Dual Path Graph Convolutional Networks. - Yunhe Li; Yaochen Hu 001; Yingxue Zhang 001 (ICASSP, 2022)

Invariant Factor Graph Neural Networks. - Zheng Fang 007; Ziyun Zhang; Guojie Song; Yingxue Zhang 001; Dong Li 016; Jianye Hao; Xi Wang (ICDM, 2022)

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation. - Yankai Chen 001; Huifeng Guo; Yingxue Zhang 001; Chen Ma 001; Ruiming Tang; Jingjie Li; Irwin King (KDD, 2022)

Generalizable Floorplanner through Corner Block List Representation and Hypergraph Embedding. - Mohammad Amini; Zhanguang Zhang; Surya Penmetsa; Yingxue Zhang 001; Jianye Hao; Wulong Liu (KDD, 2022)

Bidirectional Learning for Offline Infinite-width Model-based Optimization. - Can Chen 005; Yingxue Zhang 001; Jie Fu 001; Xue (Steve) Liu; Mark Coates (NeurIPS, 2022)

Versatile Multi-stage Graph Neural Network for Circuit Representation. - Shuwen Yang; Zhihao Yang; Dong Li 016; Yingxue Zhang 001; Zhanguang Zhang; Guojie Song; Jianye Hao (NeurIPS, 2022)

EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems. - Ishaan Kumar; Yaochen Hu 001; Yingxue Zhang 001 (SIGIR, 2022)

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation. - Yankai Chen 001; Menglin Yang 001; Yingxue Zhang 001; Mengchen Zhao; Ziqiao Meng; Jianye Hao; Irwin King (WSDM, 2022)

Polarized Graph Neural Networks. - Zheng Fang 007; Lingjun Xu; Guojie Song; Qingqing Long; Yingxue Zhang 001 (WWW, 2022)

DyG2Vec: Representation Learning for Dynamic Graphs with Self-Supervision. - Mohammad Ali Alomrani; Mahdi Biparva; Yingxue Zhang 001; Mark Coates (CoRR, 2022)

Node copying: A random graph model for effective graph sampling. - Florence Regol; Soumyasundar Pal; Jianing Sun; Yingxue Zhang 001; Yanhui Geng; Mark Coates (Signal Process., 2022)

Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network. - Mehrtash Mehrabi; Walid Masoudimansour; Yingxue Zhang 001; Jie Chuai; Zhitang Chen; Mark Coates; Jianye Hao; Yanhui Geng (AAAI, 2023)

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems. - Yuening Wang; Yingxue Zhang 001; Antonios Valkanas; Ruiming Tang; Chen Ma 001; Jianye Hao; Mark Coates (AAAI, 2023)

Spectral Augmentations for Graph Contrastive Learning. - Amur Ghose; Yingxue Zhang 001; Jianye Hao; Mark Coates (AISTATS, 2023)

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System. - Bowei He; Xu He; Renrui Zhang; Yingxue Zhang 001; Ruiming Tang; Chen Ma 001 (CIKM, 2023)

Dual-Process Graph Neural Network for Diversified Recommendation. - Yuanyi Ren; Hang Ni; Yingxue Zhang 001; Xi Wang; Guojie Song; Dong Li 016; Jianye Hao (CIKM, 2023)

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation. - Haolun Wu; Yingxue Zhang 001; Chen Ma 001; Wei Guo 006; Ruiming Tang; Xue Liu 001; Mark Coates (ICDE, 2023)

Bidirectional Learning for Offline Model-based Biological Sequence Design. - Can Chen 005; Yingxue Zhang 001; Xue Liu 001; Mark Coates (ICML, 2023)

A Survey on User Behavior Modeling in Recommender Systems. - Zhicheng He 001; Weiwen Liu; Wei Guo 006; Jiarui Qin; Yingxue Zhang 001; Yaochen Hu 001; Ruiming Tang (IJCAI, 2023)

Hierarchical Projection Enhanced Multi-behavior Recommendation. - Chang Meng; Hengyu Zhang 001; Wei Guo 006; Huifeng Guo; Haotian Liu; Yingxue Zhang 001; Hongkun Zheng; Ruiming Tang; Xiu Li 001; Rui Zhang 003 (KDD, 2023)

Compressed Interaction Graph based Framework for Multi-behavior Recommendation. - Wei Guo 006; Chang Meng; Enming Yuan; Zhicheng He 001; Huifeng Guo; Yingxue Zhang 001; Bo Chen 023; Yaochen Hu 001; Ruiming Tang; Xiu Li 001; Rui Zhang 003 (WWW, 2023)

Dynamically Expandable Graph Convolution for Streaming Recommendation. - Bowei He; Xu He; Yingxue Zhang 001; Ruiming Tang; Chen Ma 001 (WWW, 2023)

Towards Automated Negative Sampling in Implicit Recommendation. - Fuyuan Lyu; Yaochen Hu 001; Xing Tang 007; Yingxue Zhang 001; Ruiming Tang; Xue Liu 001 (CoRR, 2023)

Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems. - Hongjian Gu; Yaochen Hu 001; Yingxue Zhang 001 (CoRR, 2023)

EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems. - Mohammad Dehghan; Mohammad Ali Alomrani; Sunyam Bagga; David Alfonso-Hermelo; Khalil Bibi; Abbas Ghaddar; Yingxue Zhang 001; Xiaoguang Li; Jianye Hao; Qun Liu 001; Jimmy Lin; Boxing Chen; et al. (ACL (1), 2024)

Multi-resolution Time-Series Transformer for Long-term Forecasting. - Yitian Zhang; Liheng Ma; Soumyasundar Pal; Yingxue Zhang 001; Mark Coates (AISTATS, 2024)

Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation. - Yuening Wang; Man Chen; Yaochen Hu 001; Wei Guo 006; Yingxue Zhang 001; Huifeng Guo; Yong Liu 020; Mark Coates (CIKM, 2024)

Generalizable and Relation Sensitive Netlist Representation for Analog Circuit Design. - Surya Penmetsa; Fahad Rahman Amik; Zhanguang Zhang; Yingying Fu; Yingxue Zhang 001; Wulong Liu; Jianye Hao (ACM Great Lakes Symposium on VLSI, 2024)

CKGConv: General Graph Convolution with Continuous Kernels. - Liheng Ma; Soumyasundar Pal; Yitian Zhang; Jiaming Zhou; Yingxue Zhang 001; Mark Coates (ICML, 2024)

RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network. - Yunbo Hou; Haoran Ye; Yingxue Zhang 001; Siyuan Xu; Guojie Song (KDD, 2024)

GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection. - Zhanguang Zhang; Didier Chételat; Joseph Cotnareanu; Amur Ghose; Wenyi Xiao; Hui-Ling Zhen; Yingxue Zhang 001; Jianye Hao; Mark Coates; Mingxuan Yuan (KDD, 2024)

HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation. - Joseph Cotnareanu; Zhanguang Zhang; Hui-Ling Zhen; Yingxue Zhang 001; Mark Coates (NeurIPS, 2024)

Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers. - Raika Karimi; Faezeh Faez; Yingxue Zhang 001; Xing Li; Lei Chen 031; Mingxuan Yuan; Mahdi Biparva (CoRR, 2024)

Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data. - Jiaming Zhou; Abbas Ghaddar; Ge Zhang; Liheng Ma; Yaochen Hu 001; Soumyasundar Pal; Mark Coates; Bin Wang 034; Yingxue Zhang 001; Jianye Hao (CoRR, 2024)

ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models. - Lingfeng Zhang; Yuening Wang; Hongjian Gu; Atia Hamidizadeh; Zhanguang Zhang; Yuecheng Liu; Yutong Wang; David Gamaliel Arcos Bravo; Junyi Dong; Shunbo Zhou; Tongtong Cao; Yuzheng Zhuang; et al. (CoRR, 2024)

Enhancing CTR Prediction in Recommendation Domain with Search Query Representation. - Yuening Wang; Chen Ma 001; Yaochen Hu 001; Wei Guo 006; Yingxue Zhang 001; Huifeng Guo; Yong Liu 020; Mark Coates (CoRR, 2024)

The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation. - Reza Moravej; Saurabh Bodhe; Zhanguang Zhang; Didier Chételat; Dimitrios Tsaras; Yingxue Zhang 001; Hui-Ling Zhen; Jianye Hao; Mingxuan Yuan (CoRR, 2024)

Hint Marginalization for Improved Reasoning in Large Language Models. - Soumyasundar Pal; Didier Chételat; Yingxue Zhang 001; Mark Coates (CoRR, 2024)

Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models. - Ge Zhang; Mohammad Ali Alomrani; Hongjian Gu; Jiaming Zhou; Yaochen Hu 001; Bin Wang 034; Qun Liu 001; Mark Coates; Yingxue Zhang 001; Jianye Hao (CoRR, 2024)

DyG2Vec: Efficient Representation Learning for Dynamic Graphs. - Mohammad Ali Alomrani; Mahdi Biparva; Yingxue Zhang 001; Mark Coates (Trans. Mach. Learn. Res., 2024)

Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization. - Mahdi Biparva; Raika Karimi; Faezeh Faez; Yingxue Zhang 001 (Trans. Mach. Learn. Res., 2024)

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation. - Chang Meng; Ziqi Zhao; Wei Guo 006; Yingxue Zhang 001; Haolun Wu; Chen Gao 001; Dong Li 016; Xiu Li 001; Ruiming Tang (ACM Trans. Inf. Syst., 2024)

InnerThoughts: Disentangling Representations and Predictions in Large Language Models. - Didier Chételat; Joseph Cotnareanu; Rylee Thompson; Yingxue Zhang 001; Mark Coates (AISTATS, 2025)

MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization. - Faezeh Faez; Raika Karimi; Yingxue Zhang 001; Xing Li; Lei Chen 031; Mingxuan Yuan; Mahdi Biparva (ASP-DAC, 2025)

TransPlace: Transferable Circuit Global Placement via Graph Neural Network. - Yunbo Hou; Haoran Ye; Shuwen Yang; Yingxue Zhang 001; Siyuan Xu; Guojie Song (KDD (1), 2025)

Sparse Decomposition of Graph Neural Networks. - Yaochen Hu 001; Mai Zeng; Ge Zhang; Pavel Rumiantsev; Liheng Ma; Yingxue Zhang 001; Mark Coates (Trans. Mach. Learn. Res., 2025)

Personalized Negative Reservoir for Incremental Learning in Recommender Systems. - Antonios Valkanas; Yuening Wang; Yingxue Zhang 001; Mark Coates (Trans. Mach. Learn. Res., 2025)

TransPlace: Transferable Circuit Global Placement via Graph Neural Network. - Yunbo Hou; Haoran Ye; Yingxue Zhang 001; Siyuan Xu; Guojie Song (CoRR, 2025)

E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction. - Saurabh Bodhe; Zhanguang Zhang; Atia Hamidizadeh; Shixiong Kai; Yingxue Zhang 001; Mingxuan Yuan (CoRR, 2025)

SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning. - Yuecheng Liu; Dafeng Chi; Shiguang Wu 004; Zhanguang Zhang; Yaochen Hu 001; Lingfeng Zhang; Yingxue Zhang 001; Shuang Wu 005; Tongtong Cao; Guowei Huang; Helong Huang; Guangjian Tian; et al. (CoRR, 2025)

Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation. - Lingfeng Zhang; Yuecheng Liu; Zhanguang Zhang; Matin Aghaei; Yaochen Hu 001; Hongjian Gu; Mohammad Ali Alomrani; David Gamaliel Arcos Bravo; Raika Karimi; Atia Hamidizadeh; Haoping Xu; Guowei Huang; et al. (CoRR, 2025)

Simplifying Graph Transformers. - Liheng Ma; Soumyasundar Pal; Yingxue Zhang 001; Philip H. S. Torr; Mark Coates (CoRR, 2025)

Reinforcing Question Answering Agents with Minimalist Policy Gradient Optimization. - Yihong Wu; Liheng Ma; Muzhi Li; Jiaming Zhou; Jianye Hao; Ho-fung Leung; Irwin King; Yingxue Zhang 001; Jian-Yun Nie (CoRR, 2025)

One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration. - Jinbang Huang; Yixin Xiao; Zhanguang Zhang; Mark Coates; Jianye Hao; Yingxue Zhang 001 (CoRR, 2025)

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs. - Mohammad Ali Alomrani; Yingxue Zhang 001; Derek Li; Qianyi Sun; Soumyasundar Pal; Zhanguang Zhang; Yaochen Hu 001; Rohan Deepak Ajwani; Antonios Valkanas; Raika Karimi; Peng Cheng; Yunzhou Wang; et al. (CoRR, 2025)

Omni-Thinker: Scaling Cross-Domain Generalization in LLMs via Multi-Task RL with Hybrid Rewards. - Derek Li; Jiaming Zhou; Amirreza Kazemi; Qianyi Sun; Abbas Ghaddar; Mohammad Ali Alomrani; Liheng Ma; Yu Luo; Dong Li 016; Feng Wen; Jianye Hao; Mark Coates; et al. (CoRR, 2025)

When Engineering Outruns Intelligence: A Re-evaluation of Instruction-Guided Navigation. - Matin Aghaei; Mohammad Ali Alomrani; Yingxue Zhang 001; Mahdi Biparva (CoRR, 2025)