Paul Grigas

Orcid: 0000-0002-5617-1058

According to our database1, Paul Grigas authored at least 16 papers between 2013 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2023
On the softplus penalty for large-scale convex optimization.
Oper. Res. Lett., November, 2023

Binary Classification with Instance and Label Dependent Label Noise.
CoRR, 2023

Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach.
CoRR, 2023

2022
Smart "Predict, then Optimize".
Manag. Sci., 2022

Online Contextual Decision-Making with a Smart Predict-then-Optimize Method.
CoRR, 2022

2021
Integrated Conditional Estimation-Optimization.
CoRR, 2021

Risk Bounds and Calibration for a Smart Predict-then-Optimize Method.
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.
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.
CoRR, 2019

Generalization Bounds in the Predict-then-Optimize Framework.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods.
CoRR, 2018

2017
An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion.
SIAM J. Optim., 2017

Profit Maximization for Online Advertising Demand-Side Platforms.
Proceedings of the ADKDD'17, Halifax, NS, Canada, August 13 - 17, 2017, 2017

2016
New analysis and results for the Frank-Wolfe method.
Math. Program., 2016

2015
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives.
CoRR, 2015

2013
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods.
CoRR, 2013


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