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Conflict-Averse Gradient Descent for Multi-task learning.
Bo Liu, Xingchao
Liu, Xiaojie Jin, Peter Stone, and Qiang Liu.
In Conference on Neural
Information Processing Systems (NeurIPS), 2021, December 2021.
slides
and 9-minute presentation
github repository
The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learning objective is to minimize the average loss across all tasks. While straightforward, using this objective often results in much worse final performance for each task than learning them independently. A major challenge in optimizing a multi-task model is the conflicting gradients, where gradients of different task objectives are not well aligned so that following the average gradient direction can be detrimental to specific tasks' performance. Previous work has proposed several heuristics to manipulate the task gradients for mitigating this problem. But most of them lack convergence guarantee and/or could converge to any Pareto-stationary point.In this paper, we introduce Conflict-Averse Gradient descent (CAGrad) which minimizes the average loss function, while leveraging the worst local improvement of individual tasks to regularize the algorithm trajectory. CAGrad balances the objectives automatically and still provably converges to a minimum over the average loss. It includes the regular gradient descent (GD) and the multiple gradient descent algorithm (MGDA) in the multi-objective optimization (MOO) literature as special cases. On a series of challenging multi-task supervised learning and reinforcement learning tasks, CAGrad achieves improved performance over prior state-of-the-art multi-objective gradient manipulation methods.
@InProceedings{NeurIPS2021-Liu,
author = {Bo Liu and Xingchao Liu and Xiaojie Jin and Peter Stone and Qiang Liu},
title = {Conflict-Averse Gradient Descent for Multi-task learning},
booktitle = {Conference on Neural Information Processing Systems (NeurIPS), 2021},
location = {Virtual Only},
month = {December},
year = {2021},
abstract = {
The goal of multi-task learning is to enable more efficient learning
than single task learning by sharing model structures for a diverse
set of tasks. A standard multi-task learning objective is to
minimize the average loss across all tasks. While straightforward,
using this objective often results in much worse final performance
for each task than learning them independently. A major challenge in
optimizing a multi-task model is the conflicting gradients, where
gradients of different task objectives are not well aligned so that
following the average gradient direction can be detrimental to
specific tasks' performance. Previous work has proposed several
heuristics to manipulate the task gradients for mitigating this
problem. But most of them lack convergence guarantee and/or could
converge to any Pareto-stationary point.In this paper, we introduce
Conflict-Averse Gradient descent (CAGrad) which minimizes the
average loss function, while leveraging the worst local improvement
of individual tasks to regularize the algorithm trajectory. CAGrad
balances the objectives automatically and still provably converges
to a minimum over the average loss. It includes the regular gradient
descent (GD) and the multiple gradient descent algorithm (MGDA) in
the multi-objective optimization (MOO) literature as special
cases. On a series of challenging multi-task supervised learning and
reinforcement learning tasks, CAGrad achieves improved performance
over prior state-of-the-art multi-objective gradient manipulation
methods.
},
wwwnote={<a href="https://neurips.cc/virtual/2021/poster/27328">slides and 9-minute presentation</a><br>
<a href="https://github.com/Cranial-XIX/CAGrad">github repository</a>},
}
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