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Adrien Taylor
Adrien Taylor
Inria - ENS Paris
Adresă de e-mail confirmată pe inria.fr - Pagina de pornire
Titlu
Citat de
Citat de
Anul
Smooth strongly convex interpolation and exact worst-case performance of first-order methods
AB Taylor, JM Hendrickx, F Glineur
Mathematical Programming 161, 307-345, 2017
2032017
Exact worst-case performance of first-order methods for composite convex optimization
AB Taylor, JM Hendrickx, F Glineur
SIAM Journal on Optimization 27 (3), 1283-1313, 2017
1322017
Acceleration methods
A d'Aspremont, D Scieur, A Taylor
Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021
1112021
Optimal complexity and certification of Bregman first-order methods
RA Dragomir, AB Taylor, A d’Aspremont, J Bolte
Mathematical Programming, 1-43, 2021
842021
Operator splitting performance estimation: Tight contraction factors and optimal parameter selection
EK Ryu, AB Taylor, C Bergeling, P Giselsson
SIAM Journal on Optimization 30 (3), 2251-2271, 2020
842020
On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions
E De Klerk, F Glineur, AB Taylor
Optimization Letters 11, 1185-1199, 2017
832017
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions
A Taylor, F Bach
Proceedings of the Thirty-Second Conference on Learning Theory (COLT), 2019
762019
Exact worst-case convergence rates of the proximal gradient method for composite convex minimization
AB Taylor, JM Hendrickx, F Glineur
Journal of Optimization Theory and Applications 178, 455-476, 2018
762018
Performance estimation toolbox (PESTO): Automated worst-case analysis of first-order optimization methods
AB Taylor, JM Hendrickx, F Glineur
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 1278-1283, 2017
642017
Lyapunov functions for first-order methods: Tight automated convergence guarantees
A Taylor, B Van Scoy, L Lessard
International Conference on Machine Learning (ICML) 80, 4897--4906, 2018
572018
Convex interpolation and performance estimation of first-order methods for convex optimization.
AB Taylor
Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017
522017
Efficient first-order methods for convex minimization: a constructive approach
Y Drori, AB Taylor
Mathematical Programming 184 (1), 183-220, 2020
452020
Worst-case convergence analysis of inexact gradient and Newton methods through semidefinite programming performance estimation
E De Klerk, F Glineur, AB Taylor
SIAM Journal on Optimization 30 (3), 2053-2082, 2020
452020
An optimal gradient method for smooth strongly convex minimization
A Taylor, Y Drori
Mathematical Programming 199 (1-2), 557-594, 2023
39*2023
Complexity Guarantees for Polyak Steps with Momentum
M Barré, A Taylor, A d'Aspremont
Proceedings of the Thirty-Third Conference on Learning Theory (COLT), 2020
372020
Continuized accelerations of deterministic and stochastic gradient descents, and of gossip algorithms
M Even, R Berthier, F Bach, N Flammarion, H Hendrikx, P Gaillard, ...
Advances in Neural Information Processing Systems 34, 28054-28066, 2021
26*2021
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
B Goujaud, C Moucer, F Glineur, J Hendrickx, A Taylor, A Dieuleveut
arXiv preprint arXiv:2201.04040, 2022
222022
Prox-qp: Yet another quadratic programming solver for robotics and beyond
A Bambade, S El-Kazdadi, A Taylor, J Carpentier
RSS 2022-Robotics: Science and Systems, 2022
212022
Last-iterate convergence of optimistic gradient method for monotone variational inequalities
E Gorbunov, A Taylor, G Gidel
Advances in Neural Information Processing Systems 35, 2022
202022
Super-acceleration with cyclical step-sizes
B Goujaud, D Scieur, A Dieuleveut, AB Taylor, F Pedregosa
International Conference on Artificial Intelligence and Statistics, 3028-3065, 2022
202022
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