# Optimal Pricing for Data-Augmented AutoML Marketplaces

arXiv: https://arxiv.org/abs/2310.17843 · HTML: https://mlsanity.com/paper/2310.17843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 27 Oct 2023.

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

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017)
- [Model-based Pricing for Machine Learning in a Data Marketplace](https://mlsanity.com/paper/1805.11450/) (2018)
- [Pandora's Box Problem with Order Constraints](https://mlsanity.com/paper/2002.06968/) (2020)
- [Bayesian Agency: Linear versus Tractable Contracts](https://mlsanity.com/paper/2106.00319/) (2021)
- [Contracts with Private Cost per Unit-of-Effort](https://mlsanity.com/paper/2111.09179/) (2021)
- [Designing Menus of Contracts Efficiently: The Power of Randomization](https://mlsanity.com/paper/2202.10966/) (2022)
- [Gradient Descent based Optimization Algorithms for Deep Learning Models Training](https://mlsanity.com/paper/1903.03614/) (2019)
- [The Limits of Optimal Pricing in the Dark](https://mlsanity.com/paper/2110.01707/) (2021)

## Abstract (arXiv)

Organizations often lack sufficient data to effectively train machine learning (ML) models, while others possess valuable data that remains underutilized. Data markets promise to unlock substantial value by matching data suppliers with demand from ML consumers. However, market design involves addressing intricate challenges, including data pricing, fairness, robustness, and strategic behavior. In this paper, we propose a pragmatic data-augmented AutoML market that seamlessly integrates with existing cloud-based AutoML platforms such as Google's Vertex AI and Amazon's SageMaker. Unlike standard AutoML solutions, our design automatically augments buyer-submitted training data with valuable external datasets, pricing the resulting models based on their measurable performance improvements rather than computational costs as the status quo. Our key innovation is a pricing mechanism grounded in the instrumental value - the marginal model quality improvement - of externally sourced data. This approach bypasses direct dataset pricing complexities, mitigates strategic buyer behavior, and accommodates diverse buyer valuations through menu-based options. By integrating automated data and model discovery, our solution not only enhances ML outcomes but also establishes an economically sustainable framework for monetizing external data.