An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation. - Jie Wang 005; Lili Ju; Xiaoqiang Wang 002 (IEEE Trans. Image Process., 2009)

Image Segmentation Using Local Variation and Edge-Weighted Centroidal Voronoi Tessellations. - Jie Wang 005; Lili Ju; Xiaoqiang Wang 002 (IEEE Trans. Image Process., 2011)

VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations. - Jie Wang 005; Xiaoqiang Wang 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems. - Sen Yang 004; Jie Wang 005; Wei Fan 001; Xiatian Zhang; Peter Wonka; Jieping Ye (KDD, 2013)

Lasso Screening Rules via Dual Polytope Projection. - Jie Wang 005; Jiayu Zhou; Peter Wonka; Jieping Ye (NIPS, 2013)

Safe Screening With Variational Inequalities and Its Applicaiton to LASSO. - Jun Liu 003; Zheng Zhao; Jie Wang 005; Jieping Ye (CoRR, 2013)

Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods. - Jie Wang 005; Jun Liu 003; Jieping Ye (CoRR, 2013)

Safe Screening with Variational Inequalities and Its Application to Lasso. - Jun Liu 003; Zheng Zhao; Jie Wang 005; Jieping Ye (ICML, 2014)

A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models. - Jie Wang 005; Qingyang Li 001; Sen Yang 004; Wei Fan 001; Peter Wonka; Jieping Ye (ICML, 2014)

Scaling SVM and Least Absolute Deviations via Exact Data Reduction. - Jie Wang 005; Peter Wonka; Jieping Ye (ICML, 2014)

Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets. - Jie Wang 005; Jieping Ye (NIPS, 2014)

A Safe Screening Rule for Sparse Logistic Regression. - Jie Wang 005; Jiayu Zhou; Jun Liu 003; Peter Wonka; Jieping Ye (NIPS, 2014)

Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices. - Jie Wang 005; Jieping Ye (ICML, 2015)

Detecting genetic risk factors for Alzheimer's disease in whole genome sequence data via Lasso screening. - Tao Yang 016; Jie Wang 005; Qian Sun 002; Derrek P. Hibar; Neda Jahanshad; Li Liu 035; Yalin Wang 001; Liang Zhan; Paul M. Thompson; Jieping Ye (ISBI, 2015)

Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection. - Jie Wang 005; Jieping Ye (NIPS, 2015)

Lasso screening rules via dual polytope projection. - Jie Wang 005; Peter Wonka; Jieping Ye (J. Mach. Learn. Res., 2015)

Fused Lasso Screening Rules via the Monotonicity of Subdifferentials. - Jie Wang 005; Wei Fan 001; Jieping Ye (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Transfer Learning for Survival Analysis via Efficient L2, 1-Norm Regularized Cox Regression. - Yan Li 052; Lu Wang 004; Jie Wang 005; Jieping Ye; Chandan K. Reddy (ICDM, 2016)

Parallel Lasso Screening for Big Data Optimization. - Qingyang Li 001; Shuang Qiu; Shuiwang Ji; Paul M. Thompson; Jieping Ye; Jie Wang 005 (KDD, 2016)

A Multi-Task Learning Formulation for Survival Analysis. - Yan Li 052; Jie Wang 005; Jieping Ye; Chandan K. Reddy (KDD, 2016)

Large-Scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions. - Qingyang Li 001; Tao Yang 016; Liang Zhan; Derrek P. Hibar; Neda Jahanshad; Yalin Wang 001; Jieping Ye; Paul M. Thompson; Jie Wang 005 (MICCAI (1), 2016)

Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction. - Weizhong Zhang; Bin Hong; Jieping Ye; Deng Cai 001; Xiaofei He 001; Jie Wang 005 (CoRR, 2016)

An Efficient Algorithm For Weak Hierarchical Lasso. - Yashu Liu 001; Jie Wang 005; Jieping Ye (ACM Trans. Knowl. Discov. Data, 2016)

Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning. - Tingjin Luo; Weizhong Zhang; Shang Qiu; Yang Yang; Dongyun Yi; Guangtao Wang; Jieping Ye; Jie Wang 005 (KDD, 2017)

The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms. - Yongxin Tong; Yuqiang Chen; Zimu Zhou; Lei Chen 002; Jie Wang 005; Qiang Yang 001; Jieping Ye; Weifeng Lv (KDD, 2017)

Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression. - Qingyang Li 001; Dajiang Zhu; Jie Zhang 026; Derrek Paul Hibar; Neda Jahanshad; Yalin Wang 001; Jieping Ye; Paul M. Thompson; Jie Wang 005 (CoRR, 2017)

Identifying Genetic Risk Factors for Alzheimer's Disease via Shared Tree-Guided Feature Learning Across Multiple Tasks. - Weizhong Zhang; Tingjin Luo; Shuang Qiu; Jieping Ye; Deng Cai 001; Xiaofei He 001; Jie Wang 005 (IEEE Trans. Knowl. Data Eng., 2018)

Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets. - Jie Wang 005; Zhanqiu Zhang; Jieping Ye (J. Mach. Learn. Res., 2019)

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction. - Bin Hong; Weizhong Zhang; Wei Liu 005; Jieping Ye; Deng Cai 001; Xiaofei He 001; Jie Wang 005 (J. Mach. Learn. Res., 2019)

D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems. - Taoxing Pan; Jun Liu 003; Jie Wang 005 (AAAI, 2020)

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. - Zhanqiu Zhang; Jianyu Cai; Yongdong Zhang 001; Jie Wang 005 (AAAI, 2020)

Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization. - Qi Zhou 008; Houqiang Li; Jie Wang 005 (AAAI, 2020)

Self-Adaptive Embedding For Few-Shot Classification By Hierarchical Attention. - Xueliang Wang; Feng Wu 001; Jie Wang 005 (ICME, 2020)

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. - Zhanqiu Zhang; Jianyu Cai; Jie Wang 005 (NeurIPS, 2020)

Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method. - Qi Zhou 008; Yufei Kuang; Zherui Qiu; Houqiang Li; Jie Wang 005 (NeurIPS, 2020)

Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs. - Jiajun Chen 001; Huarui He; Feng Wu 001; Jie Wang 005 (AAAI, 2021)

Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs. - Jianyu Cai; Zhanqiu Zhang; Feng Wu 001; Jie Wang 005 (ACL/IJCNLP (Findings), 2021)

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking. - Ning Wang 020; Wengang Zhou 001; Jie Wang 005; Houqiang Li (CVPR, 2021)

Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems. - Xijun Li; Weilin Luo; Mingxuan Yuan; Jun Wang 012; Jiawen Lu; Jie Wang 005; Jinhu Lü; Jia Zeng (ICDE, 2021)

On Explainability of Graph Neural Networks via Subgraph Explorations. - Hao Yuan 001; Haiyang Yu 005; Jie Wang 005; Kang Li 004; Shuiwang Ji (ICML, 2021)

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs. - Zhanqiu Zhang; Jie Wang 005; Jiajun Chen 001; Shuiwang Ji; Feng Wu 001 (NeurIPS, 2021)

Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021. - Jianyu Cai; Jiajun Chen 001; Taoxing Pan; Zhanqiu Zhang; Jie Wang 005 (CoRR, 2021)

Separated smooth sampling for fine-grained image classification. - Shenghai Rong; Zilei Wang; Jie Wang 005 (Neurocomputing, 2021)

Interpreting the Latent Space of GANs via Measuring Decoupling. - Ziqiang Li 001; Rentuo Tao; Jie Wang 005; Fu Li; Hongjing Niu; Mingdao Yue; Bin Li 025 (IEEE Trans. Artif. Intell., 2021)

Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization. - Yufei Kuang; Miao Lu; Jie Wang 005; Qi Zhou 008; Bin Li 025; Houqiang Li (AAAI, 2022)

Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic. - Zhihai Wang; Jie Wang 005; Qi Zhou 008; Bin Li 025; Houqiang Li (AAAI, 2022)

Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables. - Huarui He; Jie Wang 005; Yunfei Liu; Feng Wu 001 (CIKM, 2022)

Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation. - Huarui He; Jie Wang 005; Zhanqiu Zhang; Feng Wu 001 (KDD, 2022)

Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions. - Rui Yang 031; Jie Wang 005; Zijie Geng; Mingxuan Ye; Shuiwang Ji; Bin Li 025; Feng Wu 001 (KDD, 2022)

Rethinking Graph Convolutional Networks in Knowledge Graph Completion. - Zhanqiu Zhang; Jie Wang 005; Jieping Ye; Feng Wu 001 (WWW, 2022)

An Improved Reinforcement Learning Algorithm for Learning to Branch. - Qingyu Qu; Xijun Li; Yunfan Zhou; Jia Zeng; Mingxuan Yuan; Jie Wang 005; Jinhu Lv; Kexin Liu; Kun Mao 001 (CoRR, 2022)

Learning to Reformulate for Linear Programming. - Xijun Li; Qingyu Qu; Fangzhou Zhu; Jia Zeng; Mingxuan Yuan; Kun Mao 001; Jie Wang 005 (CoRR, 2022)

Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities. - Xueliang Wang; Jiajun Chen 001; Feng Wu 001; Jie Wang 005 (CoRR, 2022)

Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification. - Xueliang Wang; Jianyu Cai; Shuiwang Ji; Houqiang Li; Feng Wu 001; Jie Wang 005 (CoRR, 2022)

Line Graph Neural Networks for Link Prediction. - Lei Cai; Jundong Li; Jie Wang 005; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Interpreting Image Classifiers by Generating Discrete Masks. - Hao Yuan 001; Lei Cai; Xia Hu 001; Jie Wang 005; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions. - Qiyuan Liu 008; Qi Zhou 008; Rui Yang 031; Jie Wang 005 (AAAI, 2023)

Efficient Exploration in Resource-Restricted Reinforcement Learning. - Zhihai Wang; Taoxing Pan; Qi Zhou 008; Jie Wang 005 (AAAI, 2023)

Real-Time Information Extraction for Phone Review in Car Loan Audit. - Hongxuan Liu; Jie Wang 005; Yansong Wang; Shuling Yang; Hanzhu Chen; Binbin Fang 002 (DASFAA (4), 2023)

De Novo Molecular Generation via Connection-aware Motif Mining. - Zijie Geng; Shufang Xie 003; Yingce Xia; Lijun Wu 003; Tao Qin 001; Jie Wang 005; Yongdong Zhang 001; Feng Wu 001; Tie-Yan Liu (ICLR, 2023)

LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. - Zhihao Shi; Xize Liang; Jie Wang 005 (ICLR, 2023)

Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model. - Zhihai Wang; Xijun Li; Jie Wang 005; Yufei Kuang; Mingxuan Yuan; Jia Zeng; Yongdong Zhang 001; Feng Wu 001 (ICLR, 2023)

A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability. - Zijie Geng; Xijun Li; Jie Wang 005; Xiao Li; Yongdong Zhang 001; Feng Wu 001 (NeurIPS, 2023)

Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction. - Tianyu Liu; Qitan Lv; Jie Wang 005; Shuling Yang; Hanzhu Chen (NeurIPS, 2023)

State Sequences Prediction via Fourier Transform for Representation Learning. - Mingxuan Ye; Yufei Kuang; Jie Wang 005; Yang Rui; Wengang Zhou 001; Houqiang Li; Feng Wu 001 (NeurIPS, 2023)

Learning robust representation for reinforcement learning with distractions by reward sequence prediction. - Qi Zhou 008; Jie Wang 005; Qiyuan Liu 008; Yufei Kuang; Wengang Zhou 001; Houqiang Li (UAI, 2023)

Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution. - Jie Wang 005; Rui Yang 031; Zijie Geng; Zhihao Shi; Mingxuan Ye; Qi Zhou 008; Shuiwang Ji; Bin Li 025; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2023)

Provably Convergent Subgraph-wise Sampling for Fast GNN Training. - Jie Wang 005; Zhihao Shi; Xize Liang; Shuiwang Ji; Bin Li 025; Feng Wu 001 (CoRR, 2023)

Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction. - Jie Wang 005; Hanzhu Chen; Qitan Lv; Zhihao Shi; Jiajun Chen 001; Huarui He; Hongtao Xie; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2023)

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias. - Zhihao Shi; Jie Wang 005; Fanghua Lu; Hanzhu Chen; Defu Lian; Zheng Wang; Jieping Ye; Feng Wu 001 (CoRR, 2023)

Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning. - Yufei Kuang; Xijun Li; Jie Wang 005; Fangzhou Zhu; Meng Lu; Zhihai Wang; Jia Zeng; Houqiang Li; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2023)

Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation. - Haoyang Liu 002; Yufei Kuang; Jie Wang 005; Xijun Li; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2023)

State Sequences Prediction via Fourier Transform for Representation Learning. - Mingxuan Ye; Yufei Kuang; Jie Wang 005; Rui Yang 031; Wengang Zhou 001; Houqiang Li; Feng Wu 001 (CoRR, 2023)

Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings. - Jie Wang 005; Zhanqiu Zhang; Zhihao Shi; Jianyu Cai; Shuiwang Ji; Feng Wu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming. - Haotian Ling; Zhihai Wang; Jie Wang 005 (AAAI, 2024)

SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph. - Hanzhu Chen; Xu Shen 001; Qitan Lv; Jie Wang 005; Xiaoqi Ni; Jieping Ye (ACL (1), 2024)

Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics. - Hongyu Liu; Haoyang Liu 002; Yufei Kuang; Jie Wang 005; Bin Li 025 (GECCO Companion, 2024)

Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework. - Yufei Kuang; Jie Wang 005; Haoyang Liu 002; Fangzhou Zhu; Xijun Li; Jia Zeng; Jianye Hao; Bin Li 025; Feng Wu 001 (ICLR, 2024)

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling. - Hong Wang 028; Zhongkai Hao; Jie Wang 005; Zijie Geng; Zhen Wang; Bin Li 025; Feng Wu 001 (ICLR, 2024)

Accelerating PDE Data Generation via Differential Operator Action in Solution Space. - Huanshuo Dong; Hong Wang 028; Haoyang Liu 002; Jian Luo 012; Jie Wang 005 (ICML, 2024)

Reinforcement Learning within Tree Search for Fast Macro Placement. - Zijie Geng; Jie Wang 005; Ziyan Liu; Siyuan Xu; Zhentao Tang; Mingxuan Yuan; Jianye Hao; Yongdong Zhang 001; Feng Wu 001 (ICML, 2024)

Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph. - Yufei Kuang; Jie Wang 005; Yuyan Zhou; Xijun Li; Fangzhou Zhu; Jianye Hao; Feng Wu 001 (ICML, 2024)

Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models. - Qitan Lv; Jie Wang 005; Hanzhu Chen; Bin Li 025; Yongdong Zhang 001; Feng Wu 001 (ICML, 2024)

A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design. - Zhihai Wang; Lei Chen 031; Jie Wang 005; Yinqi Bai; Xing Li; Xijun Li; Mingxuan Yuan; Jianye Hao; Yongdong Zhang 001; Feng Wu 001 (ICML, 2024)

A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design. - Zhihai Wang; Jie Wang 005; Dongsheng Zuo; Yunjie Ji; Xilin Xia; Yuzhe Ma; Jianye Hao; Mingxuan Yuan; Yongdong Zhang 001; Feng Wu 001 (ICML, 2024)

Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space. - Qianmei Liu; Yufei Kuang; Jie Wang 005 (IJCNN, 2024)

MILP-StuDio: MILP Instance Generation via Block Structure Decomposition. - Haoyang Liu 002; Jie Wang 005; Wanbo Zhang; Zijie Geng; Yufei Kuang; Xijun Li; Bin Li 025; Yongdong Zhang 001; Feng Wu 005 (NeurIPS, 2024)

Neural Krylov Iteration for Accelerating Linear System Solving. - Jian Luo 012; Jie Wang 005; Hong Wang 028; Huanshuo Dong; Zijie Geng; Hanzhu Chen; Yufei Kuang (NeurIPS, 2024)

Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework. - Zhihai Wang; Jie Wang 005; Qingyue Yang; Yinqi Bai; Xing Li; Lei Chen 031; Jianye Hao; Mingxuan Yuan; Bin Li 025; Yongdong Zhang 001; Feng Wu 001 (NeurIPS, 2024)

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications. - Xijun Li; Fangzhou Zhu; Hui-Ling Zhen; Weilin Luo; Meng Lu; Yimin Huang; Zhenan Fan; Zirui Zhou; Yufei Kuang; Zhihai Wang; Zijie Geng; Yang Li 197; et al. (CoRR, 2024)

Robust Preference Optimization with Provable Noise Tolerance for LLMs. - Xize Liang; Chao Chen 026; Jie Wang 005; Yue Wu; Zhihang Fu; Zhihao Shi; Feng Wu 001; Jieping Ye (CoRR, 2024)

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming. - Jie Wang 005; Zhihai Wang; Xijun Li; Yufei Kuang; Zhihao Shi; Fangzhou Zhu; Mingxuan Yuan; Jia Zeng; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2024)

Foundations and Frontiers of Graph Learning Theory. - Yu Huang; Min Zhou 006; Menglin Yang 001; Zhen Wang; Muhan Zhang; Jie Wang 005; Hong Xie 004; Hao Wang 076; Defu Lian; Enhong Chen (CoRR, 2024)

Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms. - Zhihai Wang; Zijie Geng; Zhaojie Tu; Jie Wang 005; Yuxi Qian; Zhexuan Xu; Ziyan Liu; Siyuan Xu; Zhentao Tang; Shixiong Kai; Mingxuan Yuan; Jianye Hao; et al. (CoRR, 2024)

Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities. - Hong Xie 004; Jinyu Mo; Defu Lian; Jie Wang 005; Enhong Chen (CoRR, 2024)

Deep Tree-based Retrieval for Efficient Recommendation: Theory and Method. - Ze Liu; Jin Zhang 035; Chao Feng 008; Defu Lian; Jie Wang 005; Enhong Chen (CoRR, 2024)

SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs. - Hanzhu Chen; Xu Shen 001; Qitan Lv; Jie Wang 005; Xiaoqi Ni; Jieping Ye (CoRR, 2024)

MILP-StuDio: MILP Instance Generation via Block Structure Decomposition. - Haoyang Liu 002; Jie Wang 005; Wanbo Zhang; Zijie Geng; Yufei Kuang; Xijun Li; Bin Li 025; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2024)

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions. - Rui Yang 031; Jie Wang 005; Guoping Wu; Bin Li 025 (CoRR, 2024)

FlowX: Towards Explainable Graph Neural Networks via Message Flows. - Shurui Gui; Hao Yuan 001; Jie Wang 005; Qicheng Lao; Kang Li 004; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Label Deconvolution for Node Representation Learning on Large-Scale Attributed Graphs Against Learning Bias. - Zhihao Shi; Jie Wang 005; Fanghua Lu; Hanzhu Chen; Defu Lian; Zheng Wang; Jieping Ye; Feng Wu 005 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming. - Jie Wang 005; Zhihai Wang; Xijun Li; Yufei Kuang; Zhihao Shi; Fangzhou Zhu; Mingxuan Yuan; Jia Zeng; Yongdong Zhang 001; Feng Wu 005 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming. - Haoyang Liu 002; Jie Wang 005; Zijie Geng; Xijun Li; Yuxuan Zong; Fangzhou Zhu; Jianye Hao; Feng Wu 001 (ICLR, 2025)

A Graph Enhanced Symbolic Discovery Framework For Efficient Logic Optimization. - Yinqi Bai; Jie Wang 005; Lei Chen 031; Zhihai Wang; Yufei Kuang; Mingxuan Yuan; Jianye Hao; Feng Wu 001 (ICLR, 2025)

Knowledge Graph Finetuning Enhances Knowledge Manipulation in Large Language Models. - Hanzhu Chen; Xu Shen 001; Jie Wang 005; Zehao Wang; Qitan Lv; Junjie He; Rong Wu; Feng Wu; Jieping Ye (ICLR, 2025)

LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement. - Zijie Geng; Jie Wang 005; Ziyan Liu; Siyuan Xu; Zhentao Tang; Shixiong Kai; Mingxuan Yuan; Jianye Hao; Feng Wu 001 (ICLR, 2025)

Differentiable Integer Linear Programming. - Zijie Geng; Jie Wang 005; Xijun Li; Fangzhou Zhu; Jianye Hao; Bin Li 025; Feng Wu 001 (ICLR, 2025)

Accurate and Scalable Graph Neural Networks via Message Invariance. - Zhihao Shi; Jie Wang 005; Zhiwei Zhuang; Xize Liang; Bin Li 025; Feng Wu 001 (ICLR, 2025)

Computing Circuits Optimization via Model-Based Circuit Genetic Evolution. - Zhihai Wang; Jie Wang 005; Xilin Xia; Dongsheng Zuo; Lei Chen 031; Yuzhe Ma; Jianye Hao; Mingxuan Yuan; Feng Wu 001 (ICLR, 2025)

Learning Robust Representations with Long-Term Information for Generalization in Visual Reinforcement Learning. - Rui Yang 031; Jie Wang 005; Qijie Peng; Ruibo Guo; Guoping Wu; Bin Li 025 (ICLR, 2025)

CATI: A medical context-enhanced framework for diagnosis code assignment in the UK Biobank study. - Yue Shen; Jie Wang 005; Zhe Wang; Zhihao Shi; Hanzhu Chen; Zheng Wang; Yukang Jiang; Xiaopu Wang; Chuandong Cheng; Xueqin Wang; Hongtu Zhu; Jieping Ye (Artif. Intell. Medicine, 2025)

AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference. - Qingyue Yang; Jie Wang 005; Xing Li; Zhihai Wang; Chen Chen 077; Lei Chen 031; Xianzhi Yu; Wulong Liu; Jianye Hao; Mingxuan Yuan; Bin Li 025 (CoRR, 2025)

Long-Term Feature Extraction via Frequency Prediction for Efficient Reinforcement Learning. - Jie Wang 005; Mingxuan Ye; Yufei Kuang; Rui Yang 031; Wengang Zhou 001; Houqiang Li; Feng Wu 005 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking. - Runquan Gui; Zhihai Wang; Jie Wang 005; Chi Ma; Hui-Ling Zhen; Mingxuan Yuan; Jianye Hao; Defu Lian; Enhong Chen; Feng Wu 001 (CoRR, 2025)

Accelerating Large Language Model Reasoning via Speculative Search. - Zhihai Wang; Jie Wang 005; Jilai Pan; Xilin Xia; Huiling Zhen; Mingxuan Yuan; Jianye Hao; Feng Wu 001 (CoRR, 2025)

Advancing Symbolic Discovery on Unsupervised Data: A Pre-training Framework for Non-degenerate Implicit Equation Discovery. - Yufei Kuang; Jie Wang 005; Huang Haotong; Mingxuan Ye; Fangzhou Zhu; Xijun Li; Jianye Hao; Wu Feng (CoRR, 2025)

One Step Beyond: Feedthrough & Placement-Aware Rectilinear Floorplanner. - Zhexuan Xu; Jie Wang 005; Siyuan Xu; Zijie Geng; Mingxuan Yuan; Feng Wu 001 (CoRR, 2025)

Piano: A Multi-Constraint Pin Assignment-Aware Floorplanner. - Zhexuan Xu; Kexin Zhou; Jie Wang 005; Zijie Geng; Siyuan Xu; Shixiong Kai; Mingxuan Yuan; Feng Wu 001 (CoRR, 2025)

Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning. - Yufei Kuang; Xijun Li; Jie Wang 005; Fangzhou Zhu; Meng Lu; Zhihai Wang; Jia Zeng; Houqiang Li; Yongdong Zhang 001; Feng Wu 005 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)