| Smart “predict, then optimize” AN Elmachtoub, P Grigas Management Science 68 (1), 9-26, 2022 | 717 | 2022 |
| New analysis and results for the Frank–Wolfe method RM Freund, P Grigas Mathematical Programming 155 (1), 199-230, 2016 | 188 | 2016 |
| 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 | 132 | 2017 |
| 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 | 90 | 2019 |
| 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 | 50 | 2017 |
| Integrated conditional estimation-optimization M Qi, P Grigas, ZJM Shen arXiv preprint arXiv:2110.12351, 2021 | 29* | 2021 |
| 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 | 24 | 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 | 22 | 2017 |
| Adaboost and forward stagewise regression are first-order convex optimization methods RM Freund, P Grigas, R Mazumder arXiv preprint arXiv:1307.1192, 2013 | 19 | 2013 |
| 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 | 15 | 2022 |
| 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 | 9 | 2023 |
| Joint online learning and decision-making via dual mirror descent A Lobos, P Grigas, Z Wen International Conference on Machine Learning, 7080-7089, 2021 | 7 | 2021 |
| 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 | 7 | 2019 |
| 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 | 7 | 2013 |
| 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 | 6 | 2018 |
| 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 | 6 | 2018 |
| Online contextual decision-making with a smart predict-then-optimize method H Liu, P Grigas arXiv preprint arXiv:2206.07316, 2022 | 4 | 2022 |
| 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 | 3 | 2021 |
| Methods for convex optimization and statistical learning PPE Grigas Massachusetts Institute of Technology, 2016 | 3 | 2016 |
| New penalized stochastic gradient methods for linearly constrained strongly convex optimization M Li, P Grigas, A Atamturk arXiv preprint arXiv:2202.07155, 2022 | 2 | 2022 |
Adam N. ElmachtoubColumbia University, Dept. of Industrial Engineering and Operations ResearchVerified email at ieor.columbia.edu
