# Generating Skyline Datasets for Data Science Models

arXiv: https://arxiv.org/abs/2502.11262 · HTML: https://mlsanity.com/paper/2502.11262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [The no-free-lunch theorems of supervised learning](https://mlsanity.com/paper/2202.04513/) (2022)
- [Submodular Maximization Meets Streaming: Matchings, Matroids, and More](https://mlsanity.com/paper/1309.2038/) (2013)
- [Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar](https://mlsanity.com/paper/1905.10345/) (2019)
- [AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model](https://mlsanity.com/paper/2001.11158/) (2020)
- [Blend: A Unified Data Discovery System](https://mlsanity.com/paper/2310.02656/) (2023)
- [Kitana: Efficient Data Augmentation Search for AutoML](https://mlsanity.com/paper/2305.10419/) (2023)

## Built on by (most cited first, 1 of 1)

- [ML-Asset Management: Curation, Discovery, and Utilization](https://mlsanity.com/paper/2509.23577/) (2025)

## Abstract (arXiv)

Preparing high-quality datasets required by various data-driven AI and machine learning models has become a cornerstone task in data-driven analysis. Conventional data discovery methods typically integrate datasets towards a single pre-defined quality measure that may lead to bias for downstream tasks. This paper introduces MODis, a framework that discovers datasets by optimizing multiple user-defined, model-performance measures. Given a set of data sources and a model, MODis selects and integrates data sources into a skyline dataset, over which the model is expected to have the desired performance in all the performance measures. We formulate MODis as a multi-goal finite state transducer, and derive three feasible algorithms to generate skyline datasets. Our first algorithm adopts a "reduce-from-universal" strategy, that starts with a universal schema and iteratively prunes unpromising data. Our second algorithm further reduces the cost with a bi-directional strategy that interleaves data augmentation and reduction. We also introduce a diversification algorithm to mitigate the bias in skyline datasets. We experimentally verify the efficiency and effectiveness of our skyline data discovery algorithms, and showcase their applications in optimizing data science pipelines.