Publications (by topic)

Book Edited

  1. The Elements of Joint Learning and Optimization in Operations Management
    Xi Chen, Stefanus Jasin, and Cong Shi (co-edited).
    Springer New York, 2022.

Exploration on Blockchain Technology and Quant Research

  1. Delta Hedging Liquidity Positions on Automated Market Makers
    Akhilesh (Adam) Khakhar and Xi Chen. Crypto Economics Security Conference, UC Berkeley.

  2. Bayesian-Nash-Incentive-Compatible Mechanism for Blockchain Transaction Fee Allocation
    Zishuo Zhao, Xi Chen and Yuan Zhou. Crypto Economics Security Conference, UC Berkeley.

  3. Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data
    Xi Chen, Yang Ha Cho, Yiwei Dou, and Baruch Lev.
    Journal of Accounting Research, 60(2), 467–515, 2022.

  4. Multiparty Democracy in Decentralized Autonomous Organization (DAO): Evidence from MakerDAO
    Xiaotong Sun, Xi Chen, Charalampos Stasinakis, Georgios Sermpinis.

  5. SoK: Play-to-Earn Projects
    Jingfan Yu, Mengqian Zhang, Xi Chen, and Zhixuan Fang.

  6. DoubleEnsemble A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
    Chuheng Zhang, Yifei Jin, Yuanqi Li, Jian Li, Xi Chen, and Pingzhong Tang.
    International Conference on Data Mining (ICDM), 2020.

Statistical Inference and Machine Learning

  1. Majority Vote for Distributed Differentially Private Sign Selection
    Weidong Liu, Jiyuan Tu, Xiaojun Mao, and Xi Chen.

  2. Two-stage Hypothesis Tests for Variable Interactions with FDR Control
    Jingyi Duan, Yang Ning, Xi Chen, and Yong Chen.

  3. Distributed Estimation and Inference for Semi-parametric Binary Response Models
    Xi Chen, Wenbo Jing, Weidong Liu, and Yichen Zhang.

  4. Online Statistical Inference for Stochastic Optimization via Gradient-free Kiefer-Wolfowitz Methods
    Xi Chen, Zehua Lai, He Li, and Yichen Zhang.

  5. Active Learning for Contextual Search with Binary Feedbacks
    Xi Chen, Quanquan Liu, and Yining Wang.
    Management Science (to appear), 2022.

  6. Dimension Independent Excess Risk by Stochastic Gradient Descent
    Xi Chen, Qiang Liu, and Xin T. Tong
    Electronic Journal of Statistics, 16(2), 4547–4603, 2022.

  7. Shape-Enforcing Operators for Point and Interval Estimators
    Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, and Ye Luo.
    Journal of Machine Learning Research, 22(220), 1–42, 2021.

  8. Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
    Jiyuan Tu, Weidong Liu, Xiaojun Mao, and Xi Chen.
    Journal of Machine Learning Research, 2021.

  9. Online Covariance Matrices Estimation of Stochastic Gradient Descent
    Wanrong Zhu, Xi Chen, and Wei Biao Wu.
    Journal of the American Statistical Association (Theory and Methods), 2021.

  10. First-order Newton-type Estimator for Distributed Estimation and Inference
    Xi Chen, Weidong Liu, and Yichen Zhang.
    Journal of the American Statistical Association (Theory and Methods), 2021.

  11. Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
    Xi Chen, Jason D. Lee, He Li, and Yun Yang.
    Journal of the American Statistical Association (Theory and Methods), 2021.

  12. Distributed High-dimensional Regression Under a Quantile Loss Function
    Xi Chen, Weidong Liu, Xiaojun Mao, and Zhuoyi Yang.
    Journal of Machine Learning Research, 21(182), 1–43, 2020.

  13. Robust inference via multiplier bootstrap
    Xi Chen, and Wen-xin Zhou.
    Annals of Statistics, 48(3): 1665–1691 2020. [Code]

  14. Statistical Inference for Model Parameters in Stochastic Gradient Descent
    Xi Chen, Jason D. Lee, Xin T. Tong, and Yichen Zhang.
    Annals of Statistics, 48(1): 251–273, 2020. [video]

  15. Quantile Regression Under Memory Constraint
    Xi Chen, Weidong Liu, and Yichen Zhang.
    Annals of Statistics, 47(6): 3244–3273, 2019. [Code]

  16. On Degrees of Freedom of Projection Estimators with Applications to Multivariate Shape Restricted Regression
    Xi Chen, Qihang Lin, and Bodhisattva Sen.
    Journal of the American Statistical Association (Theory and Methods), 115(529): 173–186, 2020.

  17. Distributed Inference for Linear Support Vector Machine
    Xiaozhou Wang, Zhuoyi Yang, Xi Chen, and Weidong Liu.
    Journal of Machine Learning Research, 20(113): 1–41, 2019. [Code]

  18. Graph Estimation for Matrix-variate Gaussian Data
    Xi Chen, Weidong Liu.
    Statistica Sinica, 29, 479–504, 2019.

  19. Testing Independence with High-dimensional Correlated Samples
    Xi Chen and Weidong Liu.
    Annals of Statistics, 46(2): 866-894, 2018.

  20. A Note on the Approximate Admissibility of Regularized Estimators in the Gaussian Sequence Model
    Xi Chen, Adityanand Guntuboyina, and Yuchen Zhang.
    Electronic Journal of Statistics, 11(2), 4746–4768, 2017.

  21. On Bayes Risk Lower Bounds
    Xi Chen, Adityanand Guntuboyina, and Yuchen Zhang.
    Journal of Machine Learning Research, 2016.

  22. Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
    Yuchen Zhang, Xi Chen, Dengyong Zhou, and Michael I. Jordan.
    Journal of Machine Learning Research, 2016. (Conference version appears in NeurIPS 2014), [Code]

Bandit and Reinforcement Learning with Applications to Revenue Management and Crowdsourcing

  1. Fairness-aware Online Price Discrimination with Nonparametric Demand Models
    Xi Chen, Xuan Zhang, and Yuan Zhou.

  2. Fairness-aware Network Revenue Management with Demand Learning
    Xi Chen, Jiameng Lyu, Yining Wang, and Yuan Zhou.

  3. Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
    Xi Chen, Akshay Krishnamurthy, and Yining Wang.

  4. On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
    Wenlong Mou, Zheng Wen, and Xi Chen.

  5. Context–Based Dynamic Pricing with Online Clustering
    Sentao Miao, Xi Chen, Xiuli Chao, Jiaxi Liu, and Yidong Zhang.
    Production and Operations Management, 31(9), 3559–3575, 2022.

  6. Dynamic Recommendation at Checkout under Inventory Constraint
    Xi Chen, Will Ma, David Simchi-Levi, and Linwei Xin.

  7. Differential Privacy in Personalized Pricing with Nonparametric Demand Models
    Xi Chen, Sentao Miao, and Yining Wang.
    Operations Research (to appear), 2022.

  8. Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers
    Xi Chen, and Yining Wang.
    Operations Research (to appear), 2022.

  9. Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms
    Zishuo Zhao, Xi Chen, Xuefeng Zhang, and Yuan Zhou.
    International Joint Conference on Artificial Intelligence (IJCAI), 2022.

  10. Dynamic Learning and Pricing with Strategic Customers
    Xi Chen, Jianjun Gao, Dongdong Ge, and Zizhuo Wang.
    Production and Operations Management, 31(8), 3125–3142, 2022.

  11. Asymptotically Optimal Sequential Design for Rank Aggregation
    Xi Chen, Yunxiao Chen, Xiaoou Li.
    Mathematics of Operations Research, 47(3), 2310-2332, 2022.

  12. No Weighted-Regret Learning in Adversarial Bandits with Delays
    Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, and Jose Blanchet
    Journal of Machine Learning Research, 23(139), 1–43, 2022.

  13. Adversarial Combinatorial Bandits with General Non-linear Reward Functions
    Xi Chen, Yanjun Han, and Yining Wang.
    International Conference on Machine Learning (ICML), 2021.

  14. Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
    Xi Chen, David Simchi-Levi, and Yining Wang.
    Management Science, 68(7), 4878-4898, 2022.

  15. A Statistical Learning Approach to Personalization in Revenue Management
    Xi Chen, Zachary Owen, Clark Pixton, and David Simchi-Levi.
    Management Science, 68(3), 1923–1937, 2022.

  16. Optimal Stopping and Worker Selection in Crowdsourcing: an Adaptive Sequential Probability Ratio Test Framework
    Xiaoou Li, Yunxiao Chen, Xi Chen, Jingchen Liu, and Zhiliang Ying.
    Statistica Sinica, 31: 519–546, 2021.

  17. Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
    Yingkai Li, Yining Wang, Xi Chen, and Yuan Zhou.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

  18. Dynamic Assortment Selection under the Nested Logit Models
    Xi Chen, Chao Shi, Yining Wang, and Yuan Zhou.
    Production and Operations Management, 30(1), 85–102, 2021.

  19. Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
    Yining Wang, Xi Chen, Xiangyu Chang and Dongdong Ge.
    Production and Operations Management, 30(6), 1703–1717, 2021.

  20. Dynamic Assortment Optimization with Changing Contextual Information
    Xi Chen, Yining Wang, and Yuan Zhou.
    Journal of Machine Learning Research, 21(216), 1–44, 2020.

  21. Thresholding Bandit Problem with Both Duels and Pulls
    Yichong Xu, Xi Chen, Aarti Singh, and Artur Dubrawski.
    In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

  22. Bayesian Decision Process for Budget-efficient Crowdsourced Clustering
    Xiaozhou Wang, Xi Chen, Qihang Lin, and Weidong Liu.
    Proceedings of the International Joint Conference on Artificial Intelligence, 2020. [Code]

  23. EXP3 Learning in Adversarial Bandits with Delayed Feedback
    Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, and Jose Blanchet.
    In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2019.

  24. Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models
    Xi Chen, Yining Wang, and Yuan Zhou.
    Mathematics of Operations Research, 2020. (Conference version appears in NeurIPS 2018).

  25. A Note on Tight Lower Bound for MNL-Bandit Assortment Selection Models
    Xi Chen and Yining Wang.
    Operations Reserach Letters, 46(5), 534–537, 2018.

  26. Optimal Instance Adaptive Algorithm for the Top-K Ranking Problem
    Xi Chen, Sivakanth Gopi, Jieming Mao, and Jon Schneider.
    IEEE Transcations on Information Theory, 2018.

  27. An Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model
    Xi Chen, Yuanzhi Li, Jieming Mao.
    In Proceedings of ACM-SIAM Symposium on Discrete Algorithms (SODA), 2018.

  28. Adaptive Multiple-Arm Identification
    Jiecao Chen, Xi Chen, Qin Zhang, and Yuan Zhou.
    In Proceedings of International Conference on Machine Learning (ICML), 2017.

  29. Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
    Xi Chen, Kevin Jiao, and Qihang Lin.
    Journal of Machine Learning Research, 2016 [Code]

  30. Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
    Xi Chen, Qihang Lin, and Dengyong Zhou.
    Journal of Machine Learning Research, 2015

  31. Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing
    Yuan Zhou, Xi Chen, and Jian Li.
    In Proceedings of International Conference on Machine Learning (ICML), 2014

Large-scale Optimization and Applications

  1. Wasserstein Distributional Robustness and Regularization in Statistical Learning
    Rui Gao, Xi Chen, and Anton J. Kleywegt.
    Operations Research (to appear), 2022.

  2. Large-Scale Markov Decision Problems via the Linear Programming Dual
    Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, and Alan Malek.

  3. Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
    Xi Chen, Bo Jiang, Tianyi Lin, and Shuzhong Zhang.
    Journal of Machine Learning Research, 23, 1–38, 2022.

  4. Distributionally Robust Optimization with Confidence Bands for Probability Density Functions
    Xi Chen, Qihang Lin, and Guanglin Xu.
    Informs Journal on Optimization, 4(1), 65–89, 2022.

  5. Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
    Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, and Michael I. Jordan.
    In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2020.

  6. On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
    Xi Chen, Simon S. Du, and Xin T. Tong
    Journal of Machine Learning Research , 21(68), 1–41, 2020.

  7. The Discrete Moment Problem with Nonconvex Shape Constraints
    Xi Chen, Simai He, Bo Jiang, Christopher Thomas Ryan, and Teng Zhang.
    Operations Research, 69(1), 279–296, 2021. 2020

  8. Non-Stationary Stochastic Optimization with L_{p,q}-Varition Measures (Technical Note)
    Xi Chen, Yining Wang, and Yuxiang Wang.
    Operations Research, 67(6), 1752–1765, 2019

  9. Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization
    Xi Chen, Qihang Lin, and Zizhuo Wang.
    Informs Journal on Optimization, 2(1): 34–56, 2020.

  10. Optimal Design of Process Flexibility for General Production Systems
    Xi Chen, Tengyu Ma, Jiawei Zhang, and Yuan Zhou.
    Operations Research, 67(2), 516–531, 2019

  11. Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders
    Xi Chen, Jiawei Zhang, and Yuan Zhou.
    Operations Research, 63(5): 1159–1176, 2015

  12. A Trade Execution Model under a Composite Dynamic Coherent Risk Measure
    Qihang Lin, Xi Chen, and Javier Pena.
    Operations Research Letters, 2015

Works Before Joining NYU

  1. A Smoothing Stochastic Gradient Method for Composite Optimization
    Qihang Lin, Xi Chen, and Javier Pena.
    Optimization Methods and Software, 2014

  2. A Sparsity Preserving Stochastic Gradient Method for Composite Optimization
    Qihang Lin, Xi Chen, and Javier Pena.
    Computational Optimization and Applications, 58(2): 455–482, 2014

  3. High-dimensional Structured Sparse Input-output models, with applications to GWAS
    Eric P. Xing, Mladen Kolar, Seyoung Kim, and Xi Chen.
    Practical Applications of Sparse Modeling (Edited by Irina Rish, Guillermo A. Cecchi, Aurelie - Lozano, and Alexandru Niculescu-Mizil) , MIT Press, 2014

  4. Variance Reduction for Stochastic Gradient Optimization
    Chong Wang, Xi Chen, Alex Smola, and Eric Xing.
    In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2013

  5. Pairwise Ranking Aggregation in a Crowdsourced Setting
    Xi Chen, Paul N. Bennett, Kevyn Collins-Thompson, and Eric Horvitz.
    In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM), 2013 [Code]

  6. Smoothing Proximal Gradient Method for General Structured Sparse Learning
    Xi Chen, Qihang Lin, Seyoung Kim, Jamie Carbonell, and Eric P. Xing.
    Annals of Applied Statistics (AOAS), 6(2): 719–752, 2012 [Code]

  7. An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping
    Xi Chen and Han Liu.
    Statistics in Biosciences, 4(1):3–26, 2012 [Code]

  8. Regularized Dual Averaging Methods for Stochastic Optimization
    Xi Chen, Qihang Lin, and Javier Pena.
    In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2012. [appendix]

  9. Structured Sparse Canonical Correlation Analysis
    Xi Chen, Han Liu, and Jaime Carbonell.
    In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. Oral (26/400 \(\approx\) 6%)[Code]

  10. Group Sparse Additive Models
    Junming Yin, Xi Chen, and Eric P. Xing.
    In Proceedings of International Conference on Machine Learning (ICML), 2012.

  11. Adaptive Multi-task Sparse Learning with an Application to fMRI Study
    Xi Chen, Jingrui He, Rick Lawrence, and Jaime Carbonell.
    In Proceedings of SIAM International Conference on Data Mining (SDM), 2012. Oral (53/363 \(\approx\) 14%)

  12. Smoothing Proximal Gradient Method for General Structured Sparse Learning
    Xi Chen, Qihang Lin, Seyoung Kim, Jaime Carbonell, and Eric P. Xing.
    In Proceedings of Uncertainty in Artificial Intelligence (UAI), 2011

  13. Sparse Latent Semantic Analysis
    Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, and Jaime Carbonell.
    In Proceedings of SIAM International Conference on Data Mining (SDM), 2011. [Code]

  14. Direct Robust Matrix Factorization for Anomaly Detection
    Xiong Liang, Xi Chen, and Jeff Schneider.
    In Proceedings of International Conference on Data Mining (ICDM), 2011. [Code]

  15. Graph-valued Regression
    Han Liu, Xi Chen, John Lafferty, and Larry Wasserman.
    In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2010. Spotlight (73/1219 \(\approx\)6%)

  16. Multivariate Dyadic Regression Trees for Sparse Learning Problems
    Han Liu and Xi Chen.
    In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2010.

  17. Learning Preferences using Millions of Parameters by Enforcing Sparsity
    Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, and Jaime Carbonell.
    In Proceedings of International Conference on Data Mining (ICDM), 2010.

  18. Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis
    Xi Chen, Yan Liu, Han Liu, and Jaime Carbonell.
    In Proceedings of AAAI Conference on Artificial Intelligence, 2010.

  19. Time-evolving Collaborative Filtering
    Xiong Liang, Xi Chen, T.K. Huang, Jeff Schneider, and Jaime Carbonell.
    In Proceedings of SIAM International Conference on Data Mining (SDM), 2010. [Code]

  20. Nonparametric Greedy Algorithms for the Sparse Learning Problem
    Han Liu and Xi Chen.
    In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2009.

  21. Accelerated Gradient Method for Multi-Task Sparse Learning Problem
    Xi Chen, Weike Pan, James Kwok, and Jaime Carbonell.
    In Proceedings of International Conference on Data Mining (ICDM), 200