Ashia Wilson

According to our database, Ashia Wilson authored at least 24 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models.

Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson

CoRR, 2024

Adaptive Backtracking For Faster Optimization.

Joao V. Cavalcanti, Laurent Lessard, Ashia C. Wilson

CoRR, 2024

Automating Transparency Mechanisms in the Judicial System Using LLMs: Opportunities and Challenges.

Ishana Shastri, Shomik Jain, Barbara Engelhardt, Ashia Wilson

CoRR, 2024

Faster Machine Unlearning via Natural Gradient Descent.

Omri Lev, Ashia Wilson

CoRR, 2024

As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making.

Shomik Jain, Dan Calacci, Ashia Wilson

CoRR, 2024

Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.

Shomik Jain, Kathleen Creel, Ashia Wilson

CoRR, 2024

Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.

Shomik Jain, Kathleen Creel, Ashia Camage Wilson

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization.

Qiang Fu, Ashia Camage Wilson

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Algorithmic Pluralism: A Structural Approach To Equal Opportunity.

Shomik Jain, Vinith M. Suriyakumar, Kathleen Creel, Ashia Wilson

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm.

Vishwak Srinivasan, Andre Wibisono, Ashia C. Wilson

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

What is a Fair Diffusion Model? Designing Generative Text-To-Image Models to Incorporate Various Worldviews.

Zoe De Simone, Angie W. Boggust, Arvind Satyanarayan, Ashia Wilson

CoRR, 2023

Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity.

Shomik Jain, Vinith M. Suriyakumar, Ashia Wilson

CoRR, 2023

Accelerated Stochastic Optimization Methods under Quasar-convexity.

Qiang Fu, Dongchu Xu, Ashia Camage Wilson

Proceedings of the International Conference on Machine Learning, 2023

2022

Algorithms that Approximate Data Removal: New Results and Limitations.

Vinith M. Suriyakumar, Ashia C. Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multilevel Optimization for Inverse Problems.

Simon Weissmann, Ashia Wilson, Jakob Zech

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

A Lyapunov Analysis of Accelerated Methods in Optimization.

Ashia C. Wilson, Ben Recht, Michael I. Jordan

J. Mach. Learn. Res., 2021

2020

The disparate equilibria of algorithmic decision making when individuals invest rationally.

Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes

Proceedings of the FAT* '20: Conference on Fairness, 2020

Approximate Cross-validation: Guarantees for Model Assessment and Selection.

Ashia C. Wilson, Maximilian Kasy, Lester Mackey

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions.

Ashia C. Wilson, Lester Mackey, Andre Wibisono

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

The Marginal Value of Adaptive Gradient Methods in Machine Learning.

Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Breaking Locality Accelerates Block Gauss-Seidel.

Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

A Lyapunov Analysis of Momentum Methods in Optimization.

Ashia C. Wilson, Benjamin Recht, Michael I. Jordan

CoRR, 2016

A Variational Perspective on Accelerated Methods in Optimization.

Andre Wibisono, Ashia C. Wilson, Michael I. Jordan

CoRR, 2016

2013

Streaming Variational Bayes.

Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013