1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Linearization Explains Fine-Tuning in Large Language Models
Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian +1
Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlyin…
See Me, Believe Me: Causality, Intersectionality, and Interventions Improving the Appearance of Patients
Kenya S. Andrews, Mesrob I. Ohannessian, Elena Zheleva
In the context of medical records, patients often experience testimonial injustice, where the textual account undermines the validity of their experiences. Past work has demonstrat…
Induced Model Matching: Restricted Models Help Train Full-Featured Models
Usama Muneeb, Mesrob I. Ohannessian
We consider scenarios where a very accurate (often small) predictive model using restricted features is available when training a full-featured (often larger) model. This restricte…
Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that all…
From Fair Decision Making to Social Equality
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) hav…