80 citations · 110 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…