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Paul Grigas
Paul Grigas
Assistant Professor, UC Berkeley
Geverifieerd e-mailadres voor berkeley.edu - Homepage
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Smart “predict, then optimize”
AN Elmachtoub, P Grigas
Management Science 68 (1), 9-26, 2022
5812022
New analysis and results for the Frank–Wolfe method
RM Freund, P Grigas
Mathematical Programming 155 (1), 199-230, 2016
1812016
An extended Frank--Wolfe method with “in-face” directions, and its application to low-rank matrix completion
RM Freund, P Grigas, R Mazumder
SIAM Journal on optimization 27 (1), 319-346, 2017
1252017
Generalization bounds in the predict-then-optimize framework
O El Balghiti, AN Elmachtoub, P Grigas, A Tewari
Advances in neural information processing systems 32, 2019
842019
A new perspective on boosting in linear regression via subgradient optimization and relatives
R M. Freund, P Grigas, R Mazumder
The Annals of Statistics 45 (6), 2328-2364, 2017
422017
Risk bounds and calibration for a smart predict-then-optimize method
H Liu, P Grigas
Advances in Neural Information Processing Systems 34, 22083-22094, 2021
222021
Integrated conditional estimation-optimization
M Qi, P Grigas, ZJM Shen
arXiv preprint arXiv:2110.12351, 2021
21*2021
Profit maximization for online advertising demand-side platforms
P Grigas, A Lobos, Z Wen, K Lee
Proceedings of the ADKDD'17, 1-7, 2017
202017
Adaboost and forward stagewise regression are first-order convex optimization methods
RM Freund, P Grigas, R Mazumder
arXiv preprint arXiv:1307.1192, 2013
192013
Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry …
EB Franklin, LD Yee, B Aumont, RJ Weber, P Grigas, AH Goldstein
Atmospheric Measurement Techniques 15 (12), 3779-3803, 2022
122022
Stochastic in-face frank-wolfe methods for non-convex optimization and sparse neural network training
P Grigas, A Lobos, N Vermeersch
arXiv preprint arXiv:1906.03580, 2019
72019
Incremental forward stagewise regression: Computational complexity and connections to lasso
RM Freund, P Grigas, R Mazumder
URL http://www. esat. keluwen. be/sista/ROKS2013. Available on-line, 2013
72013
Joint online learning and decision-making via dual mirror descent
A Lobos, P Grigas, Z Wen
International Conference on Machine Learning, 7080-7089, 2021
62021
Condition number analysis of logistic regression, and its implications for standard first-order solution methods
RM Freund, P Grigas, R Mazumder
arXiv preprint arXiv:1810.08727, 2018
62018
Optimal bidding, allocation and budget spending for a demand side platform under many auction types
A Lobos, P Grigas, Z Wen, K Lee
arXiv preprint arXiv:1805.11645, 2018
62018
Active learning in the predict-then-optimize framework: A margin-based approach
M Liu, P Grigas, H Liu, ZJM Shen
arXiv preprint arXiv:2305.06584, 2023
52023
Online contextual decision-making with a smart predict-then-optimize method
H Liu, P Grigas
arXiv preprint arXiv:2206.07316, 2022
42022
Methods for convex optimization and statistical learning
PPE Grigas
Massachusetts Institute of Technology, 2016
32016
New penalized stochastic gradient methods for linearly constrained strongly convex optimization
M Li, P Grigas, A Atamturk
arXiv preprint arXiv:2202.07155, 2022
22022
Optimal Bidding, Allocation, and Budget Spending for a Demand-Side Platform with Generic Auctions
P Grigas, A Lobos, Z Wen, KC Lee
Allocation, and Budget Spending for a Demand-Side Platform with Generic …, 2021
22021
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Artikelen 1–20