Accounting for Significance and Multicollinearity in Building Linear Regression Models. - Dimitris Bertsimas; Michael Lingzhi Li (CoRR, 2019)

Application of Duration-of-Stay Storage Assignment with Deep Neural Networks under Uncertainty. - Michael Lingzhi Li; Elliott Wolf; Daniel Wintz (CoRR, 2019)

Fast Exact Matrix Completion: A Unifying Optimization Framework. - Dimitris Bertsimas; Michael Lingzhi Li (CoRR, 2019)

A Hierarchy of Graph Neural Networks Based on Learnable Local Features. - Michael Lingzhi Li; Meng Dong; Jiawei Zhou; Alexander M. Rush (CoRR, 2019)

Duration-of-Stay Storage Assignment under Uncertainty. - Michael Lingzhi Li; Elliott Wolf; Daniel Wintz (ICLR, 2020)

Scalable holistic linear regression. - Dimitris Bertsimas; Michael Lingzhi Li (Oper. Res. Lett., 2020)

Slowly Varying Regression under Sparsity. - Dimitris Bertsimas; Vassilis Digalakis Jr.; Michael Lingzhi Li; Omar Skali Lami (CoRR, 2021)

Distributionally Robust Causal Inference with Observational Data. - Dimitris Bertsimas; Kosuke Imai; Michael Lingzhi Li (CoRR, 2022)

Stochastic Cutting Planes for Data-Driven Optimization. - Dimitris Bertsimas; Michael Lingzhi Li (INFORMS J. Comput., 2022)

Integrated multimodal artificial intelligence framework for healthcare applications. - Luis R. Soenksen; Yu Ma; Cynthia Zeng; Léonard Boussioux; Kimberly Villalobos Carballo; Liangyuan Na; Holly M. Wiberg; Michael Lingzhi Li; Ignacio Fuentes; Dimitris Bertsimas (npj Digit. Medicine, 2022)

Benchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset. - Junling Liu; Peilin Zhou; Yining Hua; Dading Chong; Zhongyu Tian; Andrew Liu; Helin Wang; Chenyu You; Zhenhua Guo; Lei Zhu; Michael Lingzhi Li (NeurIPS, 2023)

Interpretable Matrix Completion: A Discrete Optimization Approach. - Dimitris Bertsimas; Michael Lingzhi Li (INFORMS J. Comput., 2023)

Data-Driven COVID-19 Vaccine Development for Janssen. - Dimitris Bertsimas; Michael Lingzhi Li; Xinggang Liu; Jennings Xu; Najat Khan (INFORMS J. Appl. Anal., 2023)

Forecasting COVID-19 and Analyzing the Effect of Government Interventions. - Michael Lingzhi Li; Hamza Tazi Bouardi; Omar Skali Lami; Thomas A. Trikalinos; Nikolaos Trichakis; Dimitris Bertsimas (Oper. Res., 2023)

The Cram Method for Efficient Simultaneous Learning and Evaluation. - Zeyang Jia; Kosuke Imai; Michael Lingzhi Li (CoRR, 2024)

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules. - Michael Lingzhi Li; Kosuke Imai (CoRR, 2024)

Learning to Cover: Online Learning and Optimization with Irreversible Decisions. - Alexandre Jacquillat; Michael Lingzhi Li (CoRR, 2024)

Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation. - Michael Lingzhi Li; Shixiang Zhu (CoRR, 2024)

Holistic deep learning. - Dimitris Bertsimas; Kimberly Villalobos Carballo; Léonard Boussioux; Michael Lingzhi Li; Alex Paskov; Ivan S. Paskov (Mach. Learn., 2024)

Slowly Varying Regression Under Sparsity. - Dimitris Bertsimas; Vassilis Digalakis; Michael Lingzhi Li; Omar Skali Lami (Oper. Res., 2025)

Branch-and-Price for Prescriptive Contagion Analytics. - Alexandre Jacquillat; Michael Lingzhi Li; Martin Ramé; Kai Wang 006 (Oper. Res., 2025)