| Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data O Banerjee, L El Ghaoui, A d'Aspremont The Journal of Machine Learning Research 9, 485-516, 2008 | 1957 | 2008 |
| A direct formulation for sparse PCA using semidefinite programming A d'Aspremont, L Ghaoui, M Jordan, G Lanckriet Advances in neural information processing systems 17, 2004 | 1273 | 2004 |
| Phase recovery, maxcut and complex semidefinite programming I Waldspurger, A d’Aspremont, S Mallat Mathematical Programming 149, 47-81, 2015 | 733 | 2015 |
| First-order methods for sparse covariance selection A d'Aspremont, O Banerjee, L El Ghaoui SIAM Journal on Matrix Analysis and Applications 30 (1), 56-66, 2008 | 437 | 2008 |
| Optimal Solutions for Sparse Principal Component Analysis. A d'Aspremont, F Bach, L El Ghaoui Journal of Machine Learning Research 9 (7), 2008 | 423 | 2008 |
| Smooth optimization with approximate gradient A d'Aspremont SIAM Journal on Optimization 19 (3), 1171-1183, 2008 | 259 | 2008 |
| Global assessment of oil and gas methane ultra-emitters T Lauvaux, C Giron, M Mazzolini, A d’Aspremont, R Duren, D Cusworth, ... Science 375 (6580), 557-561, 2022 | 246 | 2022 |
| Convex optimization techniques for fitting sparse Gaussian graphical models O Banerjee, LE Ghaoui, A d'Aspremont, G Natsoulis Proceedings of the 23rd international conference on Machine learning, 89-96, 2006 | 222 | 2006 |
| Relaxations and randomized methods for nonconvex QCQPs A d’Aspremont, S Boyd EE392o Class Notes, Stanford University 1, 1-16, 2003 | 217 | 2003 |
| Predicting abnormal returns from news using text classification R Luss, A d’Aspremont Quantitative Finance 15 (6), 999-1012, 2015 | 202 | 2015 |
| Support vector machine classification with indefinite kernels R Luss, A d'Aspremont Advances in neural information processing systems 20, 2007 | 182 | 2007 |
| Regularized nonlinear acceleration D Scieur, A d'Aspremont, F Bach Advances In Neural Information Processing Systems 29, 2016 | 172 | 2016 |
| Acceleration methods A d’Aspremont, D Scieur, A Taylor Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021 | 168 | 2021 |
| Sharpness, restart and acceleration V Roulet, A d'Aspremont Advances in Neural Information Processing Systems 30, 2017 | 155 | 2017 |
| Convex relaxations for permutation problems F Fogel, R Jenatton, F Bach, A d'Aspremont Advances in neural information processing systems 26, 2013 | 142 | 2013 |
| Integration methods and optimization algorithms D Scieur, V Roulet, F Bach, A d'Aspremont Advances in Neural Information Processing Systems 30, 2017 | 123 | 2017 |
| Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions Z Deng, P Ciais, ZA Tzompa-Sosa, M Saunois, C Qiu, C Tan, T Sun, P Ke, ... Earth System Science Data 14 (4), 1639-1675, 2022 | 122 | 2022 |
| Identifying small mean-reverting portfolios A d'Aspremont Quantitative Finance 11 (3), 351-364, 2011 | 111 | 2011 |
| Optimal complexity and certification of Bregman first-order methods RA Dragomir, AB Taylor, A d’Aspremont, J Bolte Mathematical Programming, 1-43, 2022 | 109 | 2022 |
| Testing the nullspace property using semidefinite programming A d’Aspremont, L El Ghaoui Mathematical programming 127, 123-144, 2011 | 107 | 2011 |
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