Books
Web3: Blockchain, the New Economy, and the Self-Sovereign Internet
Ken Huang, Youwei Yang, Fan Zhang, Xi Chen, and Feng Zhu.
Cambridge University Press, 2024.
The Elements of Joint Learning and Optimization in Operations Management
Xi Chen, Stefanus Jasin, and Cong Shi (co-edited).
Springer New York, 2022.
Blockchain Technology and Quant Research
Proof-of-Learning with Incentive Security
Zishuo Zhao, Zhixuan Fang, Xuechao Wang, Xi Chen, and Yuan Zhou.
It Takes Two: A Peer-Prediction Solution for Blockchain Verifier's Dilemma
Zishuo Zhao, Xi Chen, and Yuan Zhou.
Bayesian-Nash-Incentive-Compatible Mechanism for Blockchain Transaction Fee Allocation
Xi Chen, David Simchi-Levi, Zishuo Zhao, and Yuan Zhou.
Operations Research (to appear), 2025.
Best Paper Award at the NeurIPS Workshop ‘‘Decentralization and Trustworthy Machine Learning in Web3’’.
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
Haochen Zhang, Xi Chen, Lin F. Yang.
SoK: MEV Countermeasures: Theory and Practice
Sen Yang, Fan Zhang, Ken Huang, Xi Chen, Youwei Yang, and Feng Zhu.
ACM Proceedings of the Workshop on Decentralized Finance and Security (DeFi ’24), 2024.
Delta Hedging Liquidity Positions on Automated Market Makers
Akhilesh (Adam) Khakhar and Xi Chen.
The Crypto Economics Security Conference at UC Berkeley, 2023
A Framework of Transaction Packaging in High-throughput Blockchains
Yuxuan Lu, Qian Qi, Xi Chen.
Computation of Optimal MEV in Decentralized Exchanges
Mengqian Zhang, Yuhao Li, Xinyuan Sun, Elynn Chen, Xi Chen.
MEV Makes Everyone Happy under Greedy Sequencing Rule
Yuhao Li, Mengqian Zhang, Jichen Li, Elynn Y. Chen, Xi Chen, Xiaotie Deng.
The 3rd ACM CCS Workshop on Decentralized Finance and Security (ACM DeFi), 2023.
SoK: Play-to-Earn Projects
Jingfan Yu, Mengqian Zhang, Xi Chen, and Zhixuan Fang.
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.
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
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
Weidong Liu, Jiyuan Tu, Yichen Zhang, and Xi Chen.
Data-Driven Knowledge Transfer in
Batch Q* Learning
Elynn Chen, Xi Chen, Wenbo Jing.
Learning Robust Treatment Rules for Censored Data
Yifan Cui, Junyi Liu, Tao Shen, Zhengling Qi, and Xi Chen.
Distributed Tensor Principal Component Analysis
Elynn Chen, Xi Chen, Wenbo Jing, and Yichen Zhang.
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Kejie Tang, Weidong Liu, Yichen Zhang, and Xi Chen.
Two-stage Hypothesis Tests for Variable Interactions with FDR Control
Jingyi Duan, Yang Ning, Xi Chen, and Yong Chen.
Statistical Inference with Stochastic Gradient Methods under \(\phi\)-mixing Data
Ruiqi Liu, Xi Chen, and Zuofeng Shang.
High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection
Zifeng Zhao, Feiyu Jiang, Yi Yu, and Xi Chen.
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
Xi Chen, Zehua Lai, He Li, and Yichen Zhang.
Majority Vote for Distributed Differentially Private Sign Selection
Weidong Liu, Jiyuan Tu, Xiaojun Mao, and Xi Chen.
Annals of Statistics, 52(4), 1671–1690, 2024.
Distributed Estimation and Inference for Semi-parametric Binary Response Models
Xi Chen, Wenbo Jing, Weidong Liu, and Yichen Zhang.
Annals of Statistics, 52(3), 922–947, 2024.
Online Statistical Inference for Stochastic Optimization via Gradient-free Kiefer-Wolfowitz Methods
Xi Chen, Zehua Lai, He Li, and Yichen Zhang.
Journal of the American Statistical Association (Theory and Methods), 119(548), 2972–2982, 2024.
2D-Shapley: A Framework for Fragmented Data Valuation
Liu Zhihong, Hoang Anh Just, Xiangyu Chang, Xi Chen, and Ruoxi Jia
International Conference on Machine Learning (ICML), 2023
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Shuting Shen, Xi Chen, Ethan X. Fang, Junwei Lu.
ACM Conference on Economics and Computation (EC), 2023.
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), 118(541), 393–404, 2023.
Active Learning for Contextual Search with Binary Feedbacks
Xi Chen, Quanquan Liu, and Yining Wang.
Management Science (to appear), 2022.
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.
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.
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.
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), 117(540), 1858–1874, 2022.
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), 117(540), 1775–1786, 2022.
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.
Robust inference via multiplier bootstrap
Xi Chen, and Wen-xin Zhou.
Annals of Statistics, 48(3): 1665–1691 2020. [Code]
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]
Quantile Regression Under Memory Constraint
Xi Chen, Weidong Liu, and Yichen Zhang.
Annals of Statistics, 47(6): 3244–3273, 2019. [Code]
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.
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]
Graph Estimation for Matrix-variate Gaussian Data
Xi Chen, Weidong Liu.
Statistica Sinica, 29, 479–504, 2019.
Testing Independence with High-dimensional Correlated Samples
Xi Chen and Weidong Liu.
Annals of Statistics, 46(2): 866-894, 2018.
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.
On Bayes Risk Lower Bounds
Xi Chen, Adityanand Guntuboyina, and Yuchen Zhang.
Journal of Machine Learning Research, 2016.
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
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
Xi Chen, David Simchi-Levi, and Yining Wang.
Management Science (to appear), 2024.
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
Xi Chen, Mo Liu, Yining Wang, and Yuan Zhou.
Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models
Elynn Chen, Xi Chen, Lan Gao, and Jiayu Li.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models (Technical Note)
Xi Chen, Jiameng Lyu, Xuan Zhang, and Yuan Zhou.
Operations Research (to appear), 2025.
Fairness-aware Network Revenue Management with Demand Learning
Xi Chen, Jiameng Lyu, Yining Wang, and Yuan Zhou.
Production and Operations Management, 33(2), 494–511, 2024.
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
Wenlong Mou, Zheng Wen, and Xi Chen.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
Xi Chen, Sentao Miao, and Yining Wang.
Operations Research (to appear), 2023.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
Xi Chen, Akshay Krishnamurthy, and Yining Wang.
Operations Research, 72(3), 999–1015, 2024.
Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers
Xi Chen, and Yining Wang.
Operations Research (to appear), 2023.
Assortment Planning for Recommendations at Checkout under Inventory Constraints
Xi Chen, Will Ma, David Simchi-Levi, and Linwei Xin.
Mathematics of Operations Research, 49(1), 297–325, 2024.
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
Xi Chen, David Simchi-Levi, and Yining Wang.
Management Science, 68(7), 4878-4898, 2022.
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.
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.
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.
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.
Asymptotically Optimal Sequential Design for Rank Aggregation
Xi Chen, Yunxiao Chen, Xiaoou Li.
Mathematics of Operations Research, 47(3), 2310-2332, 2022.
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.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Xi Chen, Yanjun Han, and Yining Wang.
International Conference on Machine Learning (ICML), 2021.
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.
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.
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.
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.
Dynamic Assortment Optimization with Changing Contextual Information
Xi Chen, Yining Wang, and Yuan Zhou.
Journal of Machine Learning Research, 21(216), 1–44, 2020.
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).
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.
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]
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.
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.
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.
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.
Adaptive Multiple-Arm Identification
Jiecao Chen, Xi Chen, Qin Zhang, and Yuan Zhou.
In Proceedings of International Conference on Machine Learning (ICML), 2017.
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Xi Chen, Kevin Jiao, and Qihang Lin.
Journal of Machine Learning Research, 2016 [Code]
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
Xi Chen, Qihang Lin, and Dengyong Zhou.
Journal of Machine Learning Research, 2015
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
Wasserstein Distributional Robustness and Regularization in Statistical Learning
Rui Gao, Xi Chen, and Anton J. Kleywegt.
Operations Research, 72(3), 1177–1191, 2024.
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.
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.
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.
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.
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
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
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.
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
Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders
Xi Chen, Jiawei Zhang, and Yuan Zhou.
Operations Research, 63(5): 1159–1176, 2015
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
A Smoothing Stochastic Gradient Method for Composite Optimization
Qihang Lin, Xi Chen, and Javier Pena.
Optimization Methods and Software, 2014
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
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
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
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]
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]
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]
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]
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]
Group Sparse Additive Models
Junming Yin, Xi Chen, and Eric P. Xing.
In Proceedings of International Conference on Machine Learning (ICML), 2012.
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%)
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
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]
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]
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%)
Multivariate Dyadic Regression Trees for Sparse Learning Problems
Han Liu and Xi Chen.
In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2010.
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.
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.
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]
Nonparametric Greedy Algorithms for the Sparse Learning Problem
Han Liu and Xi Chen.
In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2009.
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
|