29 citations · 144 across the 30 of their papers we have counts for
4 papers · 1 filter
Public Data-Assisted Mirror Descent for Private Model Training
Ehsan Amid, Arun Ganesh, Rajiv Mathews +6
In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training. (Here, public d…
Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao +3
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model oft…
LocoProp: Enhancing BackProp via Local Loss Optimization
Ehsan Amid, Rohan Anil, Manfred K. Warmuth
Second-order methods have shown state-of-the-art performance for optimizing deep neural networks. Nonetheless, their large memory requirement and high computational complexity, com…
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond
Negin Majidi, Ehsan Amid, Hossein Talebi +1
Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration. When noise is…