AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2013)

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2015)

New analysis and results for the Frank-Wolfe method. - Robert M. Freund; Paul Grigas (Math. Program., 2016)

Profit Maximization for Online Advertising Demand-Side Platforms. - Paul Grigas; Alfonso Lobos; Zheng Wen; Kuang-chih Lee (ADKDD@KDD, 2017)

An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion. - Robert M. Freund; Paul Grigas; Rahul Mazumder (SIAM J. Optim., 2017)

Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2018)

Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training. - Paul Grigas; Alfonso Lobos; Nathan Vermeersch (CoRR, 2019)

Joint Online Learning and Decision-making via Dual Mirror Descent. - Alfonso Lobos; Paul Grigas; Zheng Wen (ICML, 2021)

Risk Bounds and Calibration for a Smart Predict-then-Optimize Method. - Heyuan Liu; Paul Grigas (NeurIPS, 2021)

Integrated Conditional Estimation-Optimization. - Paul Grigas; Meng Qi; Zuo-Jun Shen 001 (CoRR, 2021)

Online Contextual Decision-Making with a Smart Predict-then-Optimize Method. - Heyuan Liu; Paul Grigas (CoRR, 2022)

Smart "Predict, then Optimize". - Adam N. Elmachtoub; Paul Grigas (Manag. Sci., 2022)

Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach. - Mo Liu 007; Paul Grigas; Heyuan Liu; Zuo-Jun Max Shen (CoRR, 2023)

Binary Classification with Instance and Label Dependent Label Noise. - Hyungki Im; Paul Grigas (CoRR, 2023)

Generalization Bounds in the Predict-Then-Optimize Framework. - Othman El Balghiti; Adam N. Elmachtoub; Paul Grigas; Ambuj Tewari (Math. Oper. Res., 2023)

On the softplus penalty for large-scale convex optimization. - Meng Li; Paul Grigas; Alper Atamtürk (Oper. Res. Lett., 2023)

Beyond Discretization: Learning the Optimal Solution Path. - Qiran Dong; Paul Grigas; Vishal Gupta (AISTATS, 2025)

Self-supervised Penalty-Based Learning for Robust Constrained Optimization. - Wyame Benslimane; Paul Grigas (CPAIOR (1), 2025)

Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization. - Hyungki Im; Paul Grigas (INFORMS J. Comput., 2025)

New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization. - Meng Li; Paul Grigas; Alper Atamtürk (J. Optim. Theory Appl., 2025)

New Methods for Parametric Optimization via Differential Equations. - Heyuan Liu; Paul Grigas (SIAM J. Optim., 2025)

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints. - Hyungki Im; Wyame Benslimane; Paul Grigas (CoRR, 2025)