Ashia Wilson

Orcid: 0000-0001-7072-2912

Affiliations:
  • MIT, Cambridge, MA, USA


According to our database1, Ashia Wilson authored at least 24 papers between 2013 and 2024.

Collaborative distances:

Timeline

2014
2016
2018
2020
2022
2024
0
5
10
4
2
1
2
6
1
2
2
1
2
1

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models.
CoRR, 2024

Adaptive Backtracking For Faster Optimization.
CoRR, 2024

Faster Machine Unlearning via Natural Gradient Descent.
CoRR, 2024

Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.
CoRR, 2024

Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Algorithmic Pluralism: A Structural Approach To Equal Opportunity.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Automating Transparency Mechanisms in the Judicial System Using LLMs: Opportunities and Challenges.
Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making.
Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2023
What is a Fair Diffusion Model? Designing Generative Text-To-Image Models to Incorporate Various Worldviews.
CoRR, 2023

Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity.
CoRR, 2023

Accelerated Stochastic Optimization Methods under Quasar-convexity.
Proceedings of the International Conference on Machine Learning, 2023

2022
Algorithms that Approximate Data Removal: New Results and Limitations.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multilevel Optimization for Inverse Problems.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
A Lyapunov Analysis of Accelerated Methods in Optimization.
J. Mach. Learn. Res., 2021

2020
The disparate equilibria of algorithmic decision making when individuals invest rationally.
Proceedings of the FAT* '20: Conference on Fairness, 2020

Approximate Cross-validation: Guarantees for Model Assessment and Selection.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions.
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.
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.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
A Lyapunov Analysis of Momentum Methods in Optimization.
CoRR, 2016

A Variational Perspective on Accelerated Methods in Optimization.
CoRR, 2016

2013
Streaming Variational Bayes.
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


  Loading...