Adrien B. Taylor
Orcid: 0000-0003-2509-1765
According to our database1,
Adrien B. Taylor
authored at least 33 papers
between 2017 and 2023.
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Bibliography
2023
A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization.
SIAM J. Optim., September, 2023
Math. Program., May, 2023
Principled analyses and design of first-order methods with inexact proximal operators.
Math. Program., 2023
IEEE Control. Syst. Lett., 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity.
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
2022
A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectives.
Open J. Math. Optim., March, 2022
SIAM J. Math. Data Sci., 2022
Math. Program., 2022
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python.
CoRR, 2022
Proceedings of the Robotics: Science and Systems XVIII, New York City, NY, USA, June 27, 2022
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip.
CoRR, 2021
CoRR, 2021
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
2020
Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection.
SIAM J. Optim., 2020
Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Estimation.
SIAM J. Optim., 2020
Math. Program., 2020
Proceedings of the Conference on Learning Theory, 2020
2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions.
Proceedings of the Conference on Learning Theory, 2019
2018
Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization.
J. Optim. Theory Appl., 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
2017
Convex interpolation and performance estimation of first-order methods for convex optimization.
PhD thesis, 2017
Exact Worst-Case Performance of First-Order Methods for Composite Convex Optimization.
SIAM J. Optim., 2017
On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions.
Optim. Lett., 2017
Smooth strongly convex interpolation and exact worst-case performance of first-order methods.
Math. Program., 2017
Performance estimation toolbox (PESTO): Automated worst-case analysis of first-order optimization methods.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017