Professor Center for Intelligent Decision-Making and Machine Learning School of Management Xi 'an Jiaotong University Office: Wenguan building 844, No. 28 Xianning West Road, Xi 'an, China Email: yao.s.wang@gmail.com Google Scholar

About Me

I am a Professor at the Center for Intelligent Decision-Making and Machine Learning at Xi'an Jiaotong University. Prior to this, I was a faculty member at the Department of Statistics and Data Science at Xi'an Jiaotong University. My current research focuses on the interplay of machine learning and managment science. I received my Ph.D. in Applied Mathematics from Xi'an Jiaotong University in 2014, under the supervision of Prof. Zongben Xu. During my Ph.D. study, I was a visiting student at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, advised by Prof. Ming Yuan.

I am looking for highly motivated Ph.D. and Master students with strong mathematical background, who are interest in high-dimensional data analysis, machine learning and optimization.

Preprints Under Review

• Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees (2026; with K. Wang et al.).

• Personalized Dynamic Product Ranking on Digital Platforms: A Low-Rank Cascading Bandit Approach (2026; with Y. Han et al.).

• Optimal Mediation Mechanisms in Bilateral Trade (2026; with Z. Fan, W. Shen and S. Tang).

• CoxFormer enables Spatial Omics Inference with Multimodal Generative Modeling (2026; with Y. Yang et al.).

• Solving Assortment Optimization with First-Order Methods and Neural Networks: A Computational Framework and Public Benchmark (2025; with Q. Guo et al.).

• A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation (2025; with Y. Yang, X. Chi, S. Gao and K. Wang).

• Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis (2025; with K. Wang, J. Li and S.B. Lin).

• Profit-Driven Team Grouping in Social Networks (2023; with S. Tang, J. Yuan and T. Li)

• Enabling Collaborative Diagnosis through Novel Distributed Learning System with Autonomy (2022; with X. Liu, S. Tang and S.B. Lin).

Selected Published Articles

• Assortment Optimization for the Multinomial Logit Model with Repeated Customer Interactions (with N.Chen, P.Gao and C.Wang), Management Science, forthcoming, 2026.

• Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach (with D. Wang and S.B. Lin), INFORMS Journal on Computing, 2026.

• Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes (with D. Wang and S.B. Lin), INFORMS Journal on Computing, 2026.

• Deep Reinforcement Learning for Online Assortment Customization: A Data-Driven Approach (with T. Li, C. Wang and S. Tang), Production and Operations Management 35 (2), 665–684, 2026.

• The Power of Small Initialization in Noisy Low-Tubal-Rank Tensor Recovery (with Z. Liu et al.), In Proceedings of The Fourteenth International Conference on Learning Representations, 2026.

• All-day Multi-scenes Lifelong Vision-and-Language Navigation with Tucker Adaptation (with X. Wang et al.), In Proceedings of The Fourteenth International Conference on Learning Representations, 2026.

• Kernel-based L2-Boosting with Structure Constraints (with X. Guo and S.B. Lin), Journal of Machine Learning Research 26 (296), 1-37, 2025.

• Robust Tensor Completion with Side Information (with Q. Yi, Y. Yang, D. Wang and S. Tang), IEEE Transactions on Knowledge and Data Engineering, 2025.

• Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets (with S.B. Lin and D.X. Zhou), IEEE Transactions on Information Theory, 2025

• Towards popularity-aware recommendation: A multi-behavior enhanced framework with orthogonality constraint (with with Y. Han, B. Xu and S. Gao), Omega, 2025

• A Probabilistic preference learning approach for multiple criteria ranking in dynamic decision context (with S. Zhao, J. Liu, M. Kadziński and X. Liao), European Journal of Operational Research, 2025

• Advertising meets Assortment Planning: Joint Advertising and Assortment Optimization under Multinomial Logit Model (with C. Wang and S. Tang), Journal of Combinatorial Optimization 49 (2), 25, 2025.

• Effective Generalized Low-rank Tensor Contextual Bandits (with Q. Yi, Y. Yang, S. Tang and J. Liu), IEEE Transactions on Knowledge and Data Engineering, 2024

• Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent (with Z. Liu, Z. Han, Y. Tang and X.L. Zhao), IEEE Transactions on Signal Processing 72, 5470-5483 , 2024.

• Efficient Fraud Detection Using Deep Boosting Decision Trees (with B. Xu, K. Wang and X. Liao), Decision Support Systems 175, 114037, 2023.

• Regularized Online DR-Submodular Optimization (with P. Zuo and S. Tang), Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2608-2617, 2023.

• Hyperspectral Image Super-resolution via Knowledge-Driven Deep Unrolling and Transformer Embedded Convolutional Recurrent Neural Network (with K. Wang, X. Liao, J. Li and D. Meng), IEEE Transactions on Image Processing 32, 4581-4594, 2023.

• Tensor Robust PCA with Side Information: Models and Applications (with Z. Han, S. Zhang, Y. Wang and J. Yao), IEEE Transactions on Circuits and Systems for Video Technology, 2023.

• An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration (with C. Wang, Q. Yi and X. Liao), IEEE Transactions on Neural Networks and Learning Systems, 2023.

• Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices (with J. Peng, H. Zhang, J. Wang and D. Meng), IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

• Effective streaming low-tubal-rank tensor approximation via frequent directions (with Q. Yi, C. Wang and K. Wang), IEEE Transactions on Neural Networks and Learning Systems, 2023.

• A Tensor-Based Online RPCA Model for Compressive Background Subtraction (with Z. Li, Q. Zhao, S. Zhang and D. Meng), IEEE Transactions on Neural Networks and Learning Systems, 2023.

• Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications (with S.B. Lin, S. Tang and D. Wang), INFORMS Journal on Computing 34 (6), 3096-3116, 2022.

• Nystr"{o} m Regularization for Time Series Forecasting (with Z. Sun, M. Dai and S.B. Lin), Journal of Machine Learning Research 23 (312), 1-42, 2022.

• Fast and Provable Nonconvex Tensor RPCA (with H. Qiu, S. Tang, D. Meng and Q. Yao), International Conference on Machine Learning, 18211-18249, 2022.

• Robust low-tubal-rank tensor recovery from binary measurements (with J. Hou, F. Zhang, H. Qiu, J. Wang and D. Meng), IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (8), 4355-4373, 2022.

• Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation (with Y. Zhang, Z. Han and Y. Tang), IEEE Transactions on Circuits and Systems for Video Technology 32 (11), 7286-7300, 2022.

• Universal Consistency of Deep Convolutional Neural Networks (with S.B. Lin, K. Wang and D.X. Zhou), IEEE Transactions on Information Theory 68 (7), 2022.

• Assortment optimization with repeated exposures and product-dependent patience cost (with S. Tang, J. Yuan, C. Wang and L. Chen), Operations Research Letters 50 (1), 8-15, 2022.

• SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning (with K. Wang, Q. Zhao, D. Meng, X. Liao and Z. Xu), IEEE Transactions on Cybernetics 51 (3), 1556 - 1570, 2021.

• Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors (with H. Qiu and D. Meng), CVPR, 9127 - 9136, 2021.

• Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Decomposition and Spectral Unmixing (with K. Wang, X. Zhao, J. Chan, Z. Xu and D. Meng), IEEE Transactions on Geoscience and Remote Sensing 58 (11), 7654 - 7671, 2020.

• Enhanced 3DTV regularization and its applications on HSI denoising and compressed sensing (with J. Peng, Q. Xie, Q. Zhao, Y. Leung and D. Meng), IEEE Transactions on Image Processing 29, 7889 - 7903, 2020.

• Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation (with X. Chang, Y. Zhong and S. Lin), IEEE Transactions on Neural Networks and Learning Systems 30 (2), 474-485, 2019.

• Deep generative learning via variational gradient flow (with Y. Gao, Y. Jiao, Y. Wang, C. Yang and S. Zhang), ICML, 2019.

• Rescaled Boosting in Classification (with X. Liao and S. Lin), IEEE Transactions on Neural Networks and Learning Systems 30 (9), 2598-2610, 2019.

• Fastderain: A novel video rain streak removal method using directional gradient priors (with T.X. Jiang, T.Z. Huang, X.L. Zhao and L.J. Deng), IEEE Transactions on Image Processing 28 (4), 2089-2102, 2019.

• A Generalized Model for Robust Tensor Factorization with Noise Modeling by Mixture of Gaussians (with X. Chen, Z. Han, Q. Zhao, D. Meng, L. Lin and Y. Tang), IEEE Transactions on Neural Networks and Learning Systems, 1-14, 2018.

• Hyperspectral image restoration via total variation regularized low-rank tensor decomposition (with J. Peng, Q. Zhao, D. Meng, Y. Leung and X. Zhao), IEEE Journal of Selected Topics in Applied Earth Observations and Remote, 2018.

• Sparse recovery: from vectors to tensors (with D. Meng and M. Yuan), National Science Review 5 (5), 756-767, 2018.

• Compressive sensing of hyperspectral images via joint tensor tucker decomposition and weighted total variation regularization (with L. Lin, Q. Zhao, T. Yue, D. Meng and Y. Leung), IEEE Geoscience and Remote Sensing Letters 14 (12), 2457-2461, 2017.

• Tensor rpca by bayesian cp factorization with complex noise (with Q. Luo, Z. Han, X. Chen, D. Meng, D. Liang and Y. Tang), Proceedings of the IEEE International Conference on Computer Vision, 5019-5028, 2017.

• A novel tensor-based video rain streaks removal approach via utilizing discriminatively intrinsic priors (with T. Jiang, T. Huang, X. Zhao and L. Deng), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.

• Divide and conquer local average regression (with X. Chang and S.B. Lin), Electronic Journal of Statistics 11 (1), 1326-1350, 2017.

• Total variation regularized tensor RPCA for background subtraction from compressive measurements (with W. Cao, J. Sun, D. Meng, C. Yang, A. Cichocki and Z. Xu), IEEE Transactions on Image Processing 25 (9), 4075-4090, 2016.

• Shrinkage Degree in L2 -Rescale Boosting for Regression (with L. Xu, S. Lin and Z. Xu), IEEE Transactions on Neural Networks and Learning Systems 28 (8), 1851-1864, 2016.

• Nonconvex plus quadratic penalized low-rank and sparse decomposition for noisy image alignment (with X. Chen, Z. Han, Y. Tang and H. Yu), Science China Information Sciences 59 (5), 1-13, 2016.

• Robust tensor factorization with unknown noise (with X. Chen, Z. Han, Q. Zhao, D. Meng and Y. Tang), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.

• Low-rank matrix factorization under general mixture noise distributions (with X. Cao, Y. Chen, Q. Zhao, D. Meng, D. Wang and Z. Xu), Proceedings of the IEEE International Conference on Computer Vision, 1493-1501, 2015.

• A novel sparsity measure for tensor recovery (with Q. Zhao, D. Meng, X. Kong, Q. Xie, W. Cao and Z. Xu), Proceedings of the IEEE International Conference on Computer Vision, 271-279, 2015.

• L1/2 Regularization: Convergence of Iterative Half Thresholding Algorithm (with J. Zeng, S. Lin and Z. Xu), IEEE Transactions on Signal Processing 62 (9), 2317-2329, 2014.

• L1/2 regularization (with Z. Xu, H. Zhang, X. Chang and Y. Liang), Science China Information Sciences 53 (6), 1159-1169, 2010.

Group

Current members	Pursuming	Research Direction
Yishan Han	Ph.D., 2021-	Trustworthy Recommender Systems
Jiannan Li	Ph.D., 2022-	Multi-Agent Reinforcement Learning
Zhiyu Liu (co-advised with Prof. Zhi Han)	Ph.D., 2023-	Provable Nonconvex Optimization for Machine Learning
Yiyang Yang	Ph.D., 2024-	Multi-modal Artificial Intelligence for Mangement
Jie Li	Ph.D., 2024-	Information Design and Game Theory
Qing Guo (co-advised with Prof. Li Yu)	Ph.D., 2024-	Deep Learning for Assortment Optimization
Xiejian Chi	MS., 2024-	Deep Reinforcement Learning for Dynamic Assortment Selection
Zeyu Lu	MS., 2025-	Deep Dueling Bandits for Online Preference Learning
Xiangzhou Song	MS., 2025-	Deep Generative Models for Traffic Data Imputation

Former members	Thesis	Current Position
Biao Xu , 2020-2026	- Pairwise Learning in Non-Ideal Managerial Environments	TBA
Qianxin Yi, 2021-2025	- Machine Learning Methods with Contextual Information for Recommendation Systems	Associate Professor at Zhengzhou University
Tao Li, 2021-2024	- Deep Reinforcement Learning for Online Personalized Assortment Optimization	Ph.D. candidate at Hong Kong University of Science and Technology
Pengyu Zuo, 2021-2024	- A Regularized Network Revenue Model with Fairness and Diversity Considerations	Ph.D. candidate at The Chinese University of Hong Kong, Shenzhen
Zhiyuan Chen (co-advised with Prof. Jihua Zhu), 2020-2023	- Selected Topics on Low-Tubal-Rank Tensor Recovery	Research Engineer at ByteDance
Chenhao Wang, 2019- 2022	- Advertising meets Assortment Planning: Joint Advertising and Assortment Optimization under Multinomial Logit Model	Assistant Professor at Tongji University
Haiquan Qiu (co-advised with Prof. Deyu Meng), 2019-2022,	- The Representation Methods of Structural Data and Their Applications	Ph.D. candidate at Tsinghua University
Yuan Gao, 2017-2020	- Implicit Generative Models: From Algorithms to Theories	Assistant Professor at Nankai University
Tianwei Yue, 2016-2018	- Recovering High-order Tensors from Fourier Measuremtns	Co-founder and CEO of Mathos AI
Kaidong Wang (co-advised with Prof. Zongben Xu), 2015-2020	- Some Topics on Loss Functions and Regularizers in Machine Learning	Associate Professor at Xi'an Jiaotong University
Jiangjun Peng (co-advised with Prof. Deyu Meng), 2015-2018	- Research Topics on Hyperspectral Image Restoration	Associate Professor at Northwestern Polytechnical University

Courses

• Data Intelligence in Action (MEM Core Class)

• Data Mining and Knowledge Discovery (MEM Core Class)

• Business Data Analytics (MBA Core Class)

• Optimization: Theory and Algorithms (Undergraduate Class)

• Business Intelligence: Analysis and Practice (Undergraduate Class)

• Foundations of Big Data Analytics (Undergraduate Class)

• High-dimensional Data Analysis (Undergraduate Class)

• Business Artificial Intelligence (Undergraduate Special Topics Class)

Selected Talks

• Modern Reinforcement Learning Algorithms in Operations Management (Slides)

• Fast and Provable Nonconvex Tensor RPCA (Slides)

• Joint Advertising and Assortment Optimization under Multinomial Logit Model

• Ffficient Frequent Directions Algorithms for Streaming Low-Rank Approximations

• Tensor-related Methods in Complex Data Analysis