# Learning Data Manipulation for Augmentation and Weighting

arXiv: https://arxiv.org/abs/1910.12795 · HTML: https://mlsanity.com/paper/1910.12795/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 30 of 45)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Provable Dynamic Fusion for Low-Quality Multimodal Data](https://mlsanity.com/paper/2306.02050/) (2023)
- [Learning Fast Sample Re-weighting Without Reward Data](https://mlsanity.com/paper/2109.03216/) (2021)
- [AEDA: An Easier Data Augmentation Technique for Text Classification](https://mlsanity.com/paper/2108.13230/) (2021)
- [PTab: Using the Pre-trained Language Model for Modeling Tabular Data](https://mlsanity.com/paper/2209.08060/) (2022)
- [Meta-Learning to Improve Pre-Training](https://mlsanity.com/paper/2111.01754/) (2021)
- [Gradient-based Bi-level Optimization for Deep Learning: A Survey](https://mlsanity.com/paper/2207.11719/) (2022)
- [Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers](https://mlsanity.com/paper/2103.14453/) (2021)
- [Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification](https://mlsanity.com/paper/2208.02951/) (2022)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation](https://mlsanity.com/paper/2106.15078/) (2021)
- [Poisoning Attacks and Defenses on Artificial Intelligence: A Survey](https://mlsanity.com/paper/2202.10276/) (2022)
- [Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification](https://mlsanity.com/paper/2109.00523/) (2021)
- [Generalized Data Weighting via Class-level Gradient Manipulation](https://mlsanity.com/paper/2111.00056/) (2021)
- [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks](https://mlsanity.com/paper/2206.06544/) (2022)
- [Fine-tune Language Models to Approximate Unbiased In-context Learning](https://mlsanity.com/paper/2310.03331/) (2023)
- [Adaptive Training Distributions with Scalable Online Bilevel Optimization](https://mlsanity.com/paper/2311.11973/) (2023)
- [Toward a `Standard Model' of Machine Learning](https://mlsanity.com/paper/2108.07783/) (2021)
- [LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification](https://mlsanity.com/paper/2402.16515/) (2024)
- [Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification](https://mlsanity.com/paper/2410.21526/) (2024)
- [IOT: Instance-wise Layer Reordering for Transformer Structures](https://mlsanity.com/paper/2103.03457/) (2021)
- [When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation](https://mlsanity.com/paper/2203.09391/) (2022)
- [Data Augmentation for Text Generation Without Any Augmented Data](https://mlsanity.com/paper/2105.13650/) (2021)
- [Class-Level Logit Perturbation](https://mlsanity.com/paper/2209.05668/) (2022)
- [MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning](https://mlsanity.com/paper/2304.09402/) (2023)
- [Learning to Select Pivotal Samples for Meta Re-weighting](https://mlsanity.com/paper/2302.04418/) (2023)
- [BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction](https://mlsanity.com/paper/2302.06860/) (2023)

## Abstract (arXiv)

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm. Our approach builds upon a recent connection of supervised learning and reinforcement learning (RL), and adapts an off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training. Different parameterization of the "data reward" function instantiates different manipulation schemes. We showcase data augmentation that learns a text transformation network, and data weighting that dynamically adapts the data sample importance. Experiments show the resulting algorithms significantly improve the image and text classification performance in low data regime and class-imbalance problems.