5 papers
Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
Jacob L. Block, Mehryar Mohri, Aryan Mokhtari +1
We study machine unlearning in large generative models by framing the task as density ratio estimation to a target distribution rather than supervised fine-tuning. While classifier…
Machine Unlearning under Overparameterization
Jacob L. Block, Aryan Mokhtari, Sanjay Shakkottai
Machine unlearning algorithms aim to remove the influence of specific training samples, ideally recovering the model that would have resulted from training on the remaining data al…
Provable Meta-Learning with Low-Rank Adaptations
Jacob L. Block, Sundararajan Srinivasan, Liam Collins +2
The power of foundation models (FMs) lies in their capacity to learn highly expressive representations that can be adapted to a broad spectrum of tasks. However, these pretrained m…
Conditional Gradient Methods
Gábor Braun, Alejandro Carderera, Cyrille W. Combettes +4
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithm…
Quantized Decentralized Stochastic Learning over Directed Graphs
Hossein Taheri, Aryan Mokhtari, Hamed Hassani +1
We consider a decentralized stochastic learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, d…