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Liam Madden
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Optimization and learning with information streams: Time-varying algorithms and applications
E Dall'Anese, A Simonetto, S Becker, L Madden
IEEE Signal Processing Magazine 37 (3), 71-83, 2020
942020
Best approximate quantum compiling problems
L Madden, A Simonetto
ACM Transactions on Quantum Computing 3 (2), 1-29, 2022
632022
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
L Madden, E Dall'Anese, S Becker
Journal of Machine Learning Research 25 (241), 1-36, 2024
30*2024
Bounds for the tracking error of first-order online optimization methods
L Madden, S Becker, E Dall’Anese
Journal of Optimization Theory and Applications, 1-21, 2021
172021
A stochastic operator framework for optimization and learning with sub-weibull errors
N Bastianello, L Madden, R Carli, E Dall'Anese
IEEE Transactions on Automatic Control, 2024
15*2024
Online sparse subspace clustering
L Madden, S Becker, E Dall’Anese
2019 IEEE Data Science Workshop (DSW), 248-252, 2019
122019
Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Łojasiewicz Condition
S Kim, L Madden, E Dall’Anese
2022 IEEE 61st Conference on Decision and Control (CDC), 3499-3506, 2022
10*2022
Sketching the Best Approximate Quantum Compiling Problem
L Madden, A Akhriev, A Simonetto
2022 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2022
62022
Upper and lower memory capacity bounds of transformers for next-token prediction
L Madden, C Fox, C Thrampoulidis
arXiv preprint arXiv:2405.13718, 2024
42024
Memory capacity of two layer neural networks with smooth activations
L Madden, C Thrampoulidis
SIAM Journal on Mathematics of Data Science 6 (3), 679-702, 2024
22024
Memory capacity of three-layer neural networks with non-polynomial activations
L Madden
arXiv preprint arXiv:2405.13738, 2024
12024
First-Order Methods for Online and Stochastic Optimization, and Approximate Compiling
LM Madden
University of Colorado at Boulder, 2022
12022
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