Paul Grigas

According to our database, Paul Grigas authored at least 22 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints.

Hyungki Im, Wyame Benslimane, Paul Grigas

CoRR, May, 2025

New Methods for Parametric Optimization via Differential Equations.

Heyuan Liu, Paul Grigas

SIAM J. Optim., 2025

New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization.

Meng Li, Paul Grigas, Alper Atamtürk

J. Optim. Theory Appl., 2025

Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization.

Hyungki Im, Paul Grigas

INFORMS J. Comput., 2025

Self-supervised Penalty-Based Learning for Robust Constrained Optimization.

Wyame Benslimane, Paul Grigas

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2025

Beyond Discretization: Learning the Optimal Solution Path.

Qiran Dong, Paul Grigas, Vishal Gupta

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2023

On the softplus penalty for large-scale convex optimization.

Meng Li, Paul Grigas, Alper Atamtürk

Oper. Res. Lett., November, 2023

Generalization Bounds in the Predict-Then-Optimize Framework.

Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari

Math. Oper. Res., 2023

Binary Classification with Instance and Label Dependent Label Noise.

Hyungki Im, Paul Grigas

CoRR, 2023

Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach.

Mo Liu, Paul Grigas, Heyuan Liu, Zuo-Jun Max Shen

CoRR, 2023

2022

Smart "Predict, then Optimize".

Adam N. Elmachtoub, Paul Grigas

Manag. Sci., 2022

Online Contextual Decision-Making with a Smart Predict-then-Optimize Method.

Heyuan Liu, Paul Grigas

CoRR, 2022

2021

Integrated Conditional Estimation-Optimization.

Paul Grigas, Meng Qi, Zuo-Jun Shen

CoRR, 2021

Risk Bounds and Calibration for a Smart Predict-then-Optimize Method.

Heyuan Liu, Paul Grigas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Joint Online Learning and Decision-making via Dual Mirror Descent.

Alfonso Lobos, Paul Grigas, Zheng Wen

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training.

Paul Grigas, Alfonso Lobos, Nathan Vermeersch

CoRR, 2019

2018

Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods.

Robert M. Freund, Paul Grigas, Rahul Mazumder

CoRR, 2018

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

Profit Maximization for Online Advertising Demand-Side Platforms.

Paul Grigas, Alfonso Lobos, Zheng Wen, Kuang-chih Lee

Proceedings of the ADKDD'17, Halifax, NS, Canada, August 13 - 17, 2017, 2017

2016

New analysis and results for the Frank-Wolfe method.

Robert M. Freund, Paul Grigas

Math. Program., 2016

2015

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives.

Robert M. Freund, Paul Grigas, Rahul Mazumder

CoRR, 2015

2013

AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods.

Robert M. Freund, Paul Grigas, Rahul Mazumder

CoRR, 2013