  In this dissertation, we propose a systemic framework that prioritizes
informative features and examples to enhance each stage of the development
process. Specifically, we prioritize informative features and examples and
improve the performance of feature learning, data labeling, and data selection.
We first propose an approach to extract only informative features that are
inherent to solving a target task by using auxiliary out-of-distribution data.
We deactivate the noise features in the target distribution by using that in
the out-of-distribution data. Next, we introduce an approach that prioritizes
informative examples from unlabeled noisy data in order to reduce the labeling
cost of active learning. In order to solve the purity-information dilemma,
where an attempt to select informative examples induces the selection of many
noisy examples, we propose a meta-model that finds the best balance between
purity and informativeness. Lastly, we suggest an approach that prioritizes
informative examples from labeled noisy data to preserve the performance of
data selection. For labeled image noise data, we propose a data selection
method that considers the confidence of neighboring samples to maintain the
performance of the state-of-the-art Re-labeling models. For labeled text noise
data, we present an instruction selection method that takes diversity into
account for ranking the quality of instructions with prompting, thereby
enhancing the performance of aligned large language models.
  Overall, our unified framework induces the deep learning development process
robust to noisy data, thereby effectively mitigating noisy features and
examples in real-world applications.
