publications

2023

1. A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors

   Nicola Bastianello, Liam Madden, Ruggero Carli, and 1 more author

   arXiv preprint arXiv:2105.09884, 2023
2. Memory capacity of two layer neural networks with smooth activations

   Liam Madden, and Christos Thrampoulidis

   arXiv preprint arXiv:2308.02001, 2023

2022

1. Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Łojasiewicz Condition

   Seunghyun Kim, Liam Madden, and Emiliano Dall’Anese

   In IEEE Conference on Decision and Control, 2022
2. Best Approximate Quantum Compiling Problems

   Liam Madden, and Andrea Simonetto

   ACM Transactions on Quantum Computing, 2022
3. Sketching the best approximate quantum compiling problem

   Liam Madden, Albert Akhriev, and Andrea Simonetto

   In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022
4. First-Order Methods for Online and Stochastic Optimization, and Approximate Compiling

   Liam Madden

   University of Colorado at Boulder, 2022

2021

1. Bounds For the Tracking Error of First-Order Online Optimization Methods

   Liam Madden, Stephen Becker, and Emiliano Dall’Anese

   Journal of Optimization Theory and Applications, 2021
2. High-probability Convergence Bounds for Non-convex Stochastic Gradient Descent

   Liam Madden, Emiliano Dall’Anese, and Stephen Becker

   arXiv preprint arXiv:2006.05610, 2021

2020

1. Optimization and Learning With Information Streams: Time-varying algorithms and applications

   Emiliano Dall’Anese, Andrea Simonetto, Stephen Becker, and 1 more author

   IEEE Signal Processing Magazine, 2020

2019

1. Online Sparse Subspace Clustering

   Liam Madden, Stephen Becker, and Emiliano Dall’Anese

   In 2019 IEEE Data Science Workshop (DSW), 2019