Learning to Select Pivotal Samples for Meta Re-weighting
- URL: http://arxiv.org/abs/2302.04418v1
- Date: Thu, 9 Feb 2023 03:04:40 GMT
- Title: Learning to Select Pivotal Samples for Meta Re-weighting
- Authors: Yinjun Wu, Adam Stein, Jacob Gardner, Mayur Naik
- Abstract summary: We study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance.
We propose two clustering methods within our learning framework, Representation-based clustering method (RBC) and Gradient-based clustering method (GBC)
- Score: 12.73177872962048
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sample re-weighting strategies provide a promising mechanism to deal with
imperfect training data in machine learning, such as noisily labeled or
class-imbalanced data. One such strategy involves formulating a bi-level
optimization problem called the meta re-weighting problem, whose goal is to
optimize performance on a small set of perfect pivotal samples, called meta
samples. Many approaches have been proposed to efficiently solve this problem.
However, all of them assume that a perfect meta sample set is already provided
while we observe that the selections of meta sample set is performance
critical. In this paper, we study how to learn to identify such a meta sample
set from a large, imperfect training set, that is subsequently cleaned and used
to optimize performance in the meta re-weighting setting. We propose a learning
framework which reduces the meta samples selection problem to a weighted
K-means clustering problem through rigorously theoretical analysis. We propose
two clustering methods within our learning framework, Representation-based
clustering method (RBC) and Gradient-based clustering method (GBC), for
balancing performance and computational efficiency. Empirical studies
demonstrate the performance advantage of our methods over various baseline
methods.
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