mlsanityData to 14 Oct 2026

DAVED: Data Acquisition via Experimental Design for Data Markets

arXiv 2403.13893 ↗First posted 20 Mar 2024cs.LGcited by 6

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Abstract (arXiv)

The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data providers to join the market. A major challenge for a data buyer in such a market is choosing the most valuable data points from a data seller. Unlike prior work in data valuation, which assumes centralized data access, we propose a federated approach to the data acquisition problem that is inspired by linear experimental design. Our proposed data acquisition method achieves lower prediction error without requiring labeled validation data and can be optimized in a fast and federated procedure. The key insight of our work is that a method that directly estimates the benefit of acquiring data for test set prediction is particularly compatible with a decentralized market setting.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 34 of its 68 references that are in the map. First posted on arXiv 20 Mar 2024.

Builds on

34 of 68 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responsescited by 1
  2. 2025Conformal Data Contamination Tests for In-distribution Data Acquisitioncited by 1
  3. 2025How to Purchase Labels? A Cost-Effective Approach Using Active Learning Marketscited by 1
  4. 2025Benchmarking Robust Aggregation in Decentralized Gradient Marketplacescited by 0
  5. 2026Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotioncited by 0
  6. 2026Learning Distributions from Multiple Data Providerscited by 0