80 citations · 110 across the 8 of their papers we have counts for
9 papers
Memory capacity of two layer neural networks with smooth activations
Liam Madden, Christos Thrampoulidis
Determining the memory capacity of two layer neural networks with hidden neurons and input dimension (i.e., total trainable parameters), which refers to the largest…
Sketching the Best Approximate Quantum Compiling Problem
Liam Madden, Albert Akhriev, Andrea Simonetto
This paper considers the problem of quantum compilation from an optimization perspective by fixing a circuit structure of CNOTs and rotation gates then optimizing over the rotation…
Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Łojasiewicz Condition
Seunghyun Kim, Liam Madden, Emiliano Dall'Anese
This paper focuses on the online gradient and proximal-gradient methods with stochastic gradient errors. In particular, we examine the performance of the online gradient descent me…
Best Approximate Quantum Compiling Problems
Liam Madden, Andrea Simonetto
We study the problem of finding the best approximate circuit that is the closest (in some pertinent metric) to a target circuit, and which satisfies a number of hardware constraint…
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
Nicola Bastianello, Liam Madden, Ruggero Carli +1
This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms. The framework is modeled over the different challenges that these algor…
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
Liam Madden, Emiliano Dall'Anese, Stephen Becker
Stochastic gradient descent is one of the most common iterative algorithms used in machine learning and its convergence analysis is a rich area of research. Understanding its conve…
Bounds for the tracking error of first-order online optimization methods
Liam Madden, Stephen Becker, Emiliano Dall'Anese
This paper investigates online algorithms for smooth time-varying optimization problems, focusing first on methods with constant step-size, momentum, and extrapolation-length. Assu…
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Emiliano Dall'Anese, Andrea Simonetto, Stephen Becker +1
There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniq…
Online Sparse Subspace Clustering
Liam Madden, Stephen Becker, Emiliano Dall'Anese
This paper focuses on the sparse subspace clustering problem, and develops an online algorithmic solution to cluster data points on-the-fly, without revisiting the whole dataset. T…