Award title: CAREER: A Sequential Learning Framework with Applications to Learning from Crowds
Award number: 1845444
Principal investigator: Xi Chen
Start date: March 1, 2019
End date: Feb 28, 2026 (Estimated)
Students:
Yichen Zhang (Graduated from NYU, now an assistant at Purdue University)
Zhuoyi Yang (Graduated from NYU, now an Applied Research Scientist at Amazon)
He Li (Graduated from NYU, now a Research Scientist at Citadel)
Wenbo Jing (Ph.D. student at NYU)
Quanquan Liu (Short-term Postdoc, now an adjunct professor at the University of Texas, Dallas)
Mengqian Zhang (Postdoc, now a Postdoc at Yale University)
Xin Wen (Ph.D. student at NYU)
While traditional machine learning usually deals with given static data, many online data are collected via a sequence of interactions with agents such as crowd labelers or customers. The motivating applications of the project include crowd labeling tasks (which is a powerful paradigm for utilizing human wisdom to collect data labels), sequential product recommendation, and online multi-product pricing. For all these applications, online learning and sequential decision-making are indispensable to each other. The objective of this project is to develop new sequential learning algorithms with rigorous theoretical guarantees. The developed framework will not only make fundamental technical contributions but also facilitate many important applications. For example, it will greatly improve the aggregated answers from crowd labelers with a significantly reduced cost. It can enhance the revenue of business while improving the customers’ satisfaction by providing accurate recommendations. In addition, this project also facilitates the development of new courses on machine learning for business school students, which helps bring the knowledge from data science to future business leaders, and provides training to K-12 students, with an emphasis on those from underrepresented groups.
Dynamic Pricing with Adversarially-Censored Demands
Jianyu Xu, Yining Wang, Xi Chen, and Yu-Xiang Wang.
Stochastic Linear Bandits with Latent Heterogeneity
Elynn Chen, Xi Chen, Wenbo Jing, and Xiao Liu.
Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models
Elynn Chen, Xi Chen, Lan Gao, and Jiayu Li.
Data-Driven Knowledge Transfer in
Batch Q* Learning
Elynn Chen, Xi Chen, and Wenbo Jing.
Distributed Tensor Principal Component Analysis with Data Heterogeneity
Elynn Chen, Xi Chen, Wenbo Jing, and Yichen Zhang.
Journal of the American Statistical Association (Theory and Methods, to appear), 2025.
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
Xi Chen, David Simchi-Levi, and Yining Wang.
Management Science (to appear), 2025.
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, to appear), 2024.
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.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
Xi Chen, Akshay Krishnamurthy, and Yining Wang.
Operations Research, 72(3), 999–1015, 2024.
Wasserstein Distributional Robustness and Regularization in Statistical Learning
Rui Gao, Xi Chen, and Anton J. Kleywegt.
Operations Research, 72(3), 1177–1191, 2024.
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.
Active Learning for Contextual Search with Binary Feedbacks
Xi Chen, Quanquan Liu, and Yining Wang.
Management Science, 69(4), 2165–2181, 2023.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
Xi Chen, Sentao Miao, and Yining Wang.
Operations Research, 2022.
Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers
Xi Chen, and Yining Wang.
Operations Research , 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.
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
Xi Chen, David Simchi-Levi, and Yining Wang.
Management Science, 2022.
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.
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.
A Statistical Learning Approach to Personalization in Revenue Management
Xi Chen, Zachary Owen, Clark Pixton, and David Simchi-Levi.
Management Science, 2021.
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions
Xi Chen, Qihang Lin, and Guanglin Xu.
Informs Journal on Optimization, 2021.
Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models
Xi Chen, Yining Wang, and Yuan Zhou.
Mathematics of Operations Research (to appear), 2021.
Dynamic Assortment Optimization with Changing Contextual Information
Xi Chen, Yining Wang, and Yuan Zhou.
Journal of Machine Learning Research (to appear), 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.
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.
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]
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.
Robust inference via multiplier bootstrap
Xi Chen, and Wen-xin Zhou.
Annals of Statistics, 48(3): 1665–1691 2020. [Code]
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.
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.
Bayesian Decision-making for Crowd Clustering. [GitHub]
Robust Statistical Inference Toolbox. [GitHub]
Distributed Support Vector Machine Solver. [GitHub]
Associate Editor: Journal of the American Statistical Association (Theory and Methods)
Area co-Editor (Machine Learning and Data Science): Operations Research
Associate Editor: Management Science
Associate Editor: Operations Research
Associate Editor: Management Science: (Special Issue on Data-Driven Prescriptive Analytics)
Area Chair: International Conference on Machine Learning (ICML), 2020
Committee: Lanchester Prize, INFORMS, 2022
Committee: APS student paper competition, INFORMS Applied Probability Society, 2021-2022
Temple University, AIBA Workshop, 01/2025
Columbia University, DRO-IEOR Seminar, 09/2024
Duke University, Fuqua School of Business, 09/2024
Informs Annual Conference, Arizona, 10/2023
Princeton University Wilks Statistics Seminar, Princeton, 2/2023
Thirty-sixth Conference on Neural Information Processing Systems, New Orleans, 12/2022
Rutgers University, Rutgers Business School, 11/2022
INFORMS Revenue Management and Pricing Section Conference, Chicago, 06/2022
Online Seminar of Mathematical Foundations of Data Science, Jointly by Gatech, Harvard, Northwestern, PSU, Princeton, ETH, 01/2022
University of Iowa, Business Analytics Thought Leaders Symposium, Tippie College of Business, 02/2021
The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), virtual, 08/2020
The Eleventh POMS-HK International Conference, Hong Kong, 01/2020
City University of Hongkong, Hongkong, 01/2020
Informs Annual Meeting, Seattle, WA, 10/2019
Joint Statistical Meetings (JSM), Denver, CO, 7/2019
IMS-China International Conference on Statistics and Probability, Dalian, China, 07/2019
INFORMS Revenue Management and Pricing Section Conference, Stanford Graduate School of Business, 06/2019
ICSA Applied Statistics Symposium, North Carolina, 06/2019
Stanford University, Department of Statistics, 06/2019
Annual Conference of Production and Operations Management Society (POMS), Washington D.C., 05/2019
Iowa State University, Department of Statistics, 04/2019
Columbia University, Department of Statistics, 03/2019
University of Miami, Miami Business School, 03/2019
The University of Chicago, Booth School of Business, 03/2019
The PI has developed several key algorithms (including optimal stopping for budget control in crowdsourcing, distributed inference, and comparison-based optimization models) in the first year and has developed the code for these algorithms.
The PI has integrated the research closely with the education plan. The PI has initiated a series of statistics and machine learning courses on different levels, including undergraduate, full-time MBA, part-time MBA, specialized Master's program, and Ph.D. program (see Education for more details). The PI strived hard to incorporate the research outcomes from this project into the curriculum design. For example, the PI has initiated the first graduate course in NYU Stern B-School focusing on modern machine learning and sequential learning techniques and their applications to crowdsourcing. The PI brought new educational experience to high school students via the NYU cSplash program by delivering a new tutorial Introduction to Recommender System and Crowdsourcing. The PI also lectured in the CMU Summer School of Machine Learning based on the research outcomes from this award.
Full-time EMBA Course: Statistics and Data Analysis
Stern School of Business, NYU, Spring 2025
Graduate Course: Foundations of Machine Learning and Deep Learning with Applications to Business, NYU, Spring 2024
Graduate Course: Modern Artificial Intelligence, NYU, Spring 2023, Fall 2025
Graduate Course: Foundations of Machine Learning and Deep Learning with Applications to Business, NYU, Fall 2022
First graduate course in the NYU Stern School of Business focusing on machine learning and sequential learning.
Graduate Course: From Machine Learning to Decision-making with Applications to Business, Stern School of Business, NYU, Fall 2019
Graduate Course: Statistical Modeling and Applications, Stern School of Business, NYU, Summer 2020
A new course for the specialized Master's Program in Risk Management
Courant CSplash Course, Courant Institute of Mathematics, NYU, Spring 2019
The class Introduction to Recommender System and Crowdsourcing to high school students in New York area, who are interested in STEM.
CMU Summer School on Machine Learning, Tepper School of Business, Carnegie Mellon University, May 2019
Disseminate the background on crowdsourcing and the research from this Award to graduate students.
Download teaching materials
Undergraduate Course: Statistics for Business Control, Regression, and Forecasting Models,
Stern School of Business, NYU, Fall 2019, Spring 2020
Full-time MBA Course: Statistics and Data Analysis
Stern School of Business, NYU, Fall 2020
Part-time MBA Course: Statistics and Data Analysis
Stern School of Business, NYU, Fall 2020, Spring 2021, Spring 2023, Spring 2025,
Fellow of the American Statistical Association (ASA), 2025
Fellow of the Institute of Mathematical Statistics (IMS), 2024
Andre Meyer Faculty Fellow, 2022
COPSS Leadership Academy, 2022
Poets & Quants: The World's Best 40 Under 40 MBA Professors, 2021
Elected Member of the International Statistical Institute (ISI), 2021
ICSA Outstanding Young Researcher Award, 2019
Highest award by the International Chinese Statistical Association (ICSA) to young researchers “in recognition of outstanding research achievement in statistical theory, methodology, and/or applications.”
Honorable Mention in INFORMS Junior Faculty Interest Group Paper Award (JFIG), 2019
This material is based upon work supported by the National Science Foundation under Grant No. 1845444.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Last updated: 2025-08-12