3 citations · 3 across the 3 of their papers we have counts for
6 papers
Algorithms that Approximate Data Removal: New Results and Limitations
Vinith M. Suriyakumar, Ashia C. Wilson
We study the problem of deleting user data from machine learning models trained using empirical risk minimization. Our focus is on learning algorithms which return the empirical ri…
Multilevel Optimization for Inverse Problems
Simon Weissmann, Ashia Wilson, Jakob Zech
Inverse problems occur in a variety of parameter identification tasks in engineering. Such problems are challenging in practice, as they require repeated evaluation of computationa…
Approximate Cross-validation: Guarantees for Model Assessment and Selection
Ashia Wilson, Maximilian Kasy, Lester Mackey
Cross-validation (CV) is a popular approach for assessing and selecting predictive models. However, when the number of folds is large, CV suffers from a need to repeatedly refit a…
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
Lydia T. Liu, Ashia Wilson, Nika Haghtalab +3
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individu…
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
Ashia Wilson, Lester Mackey, Andre Wibisono
We present a family of algorithms, called descent algorithms, for optimizing convex and non-convex functions. We also introduce a new first-order algorithm, called rescaled gradien…
On Symplectic Optimization
Michael Betancourt, Michael I. Jordan, Ashia C. Wilson
Accelerated gradient methods have had significant impact in machine learning -- in particular the theoretical side of machine learning -- due to their ability to achieve oracle low…