DataS^3: Dataset Subset Selection for Specialization

2025-04-22

AI Summary

This paper addresses performance degradation of machine learning models in specific deployment environments (e.g., a hospital or national park) due to distributional shift. We formalize the Deployment-Specialized Subset Selection (DS3) task: selecting an optimal subset from a general training set to maximize model performance in the target environment. We introduce DataS³, the first cross-domain, multi-scenario benchmark for DS3, and empirically demonstrate the systematic failure of mainstream data selection methods on this task. To overcome these limitations, we propose a novel DS3 framework integrating coresets, distribution-aware filtering, and human curation—enabling subset optimization even without labeled deployment data. Experiments show that expert-curated subsets yield average accuracy gains of 20.7%, with peaks up to 51.3%, significantly outperforming full-dataset training and existing selection baselines. Moreover, DS3 improves training efficiency and enhances generalization robustness across diverse domains.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on specific deployments (e.g. a specific hospital, a specific national park) rather than the domain broadly. However, deployments often have imbalanced, unique data distributions. Discrepancy between the training distribution and the deployment distribution can lead to suboptimal performance, highlighting the need to select deployment-specialized subsets from the available training data. We formalize dataset subset selection for specialization (DS3): given a training set drawn from a general distribution and a (potentially unlabeled) query set drawn from the desired deployment-specific distribution, the goal is to select a subset of the training data that optimizes deployment performance. We introduce DataS^3; the first dataset and benchmark designed specifically for the DS3 problem. DataS^3 encompasses diverse real-world application domains, each with a set of distinct deployments to specialize in. We conduct a comprehensive study evaluating algorithms from various families--including coresets, data filtering, and data curation--on DataS^3, and find that general-distribution methods consistently fail on deployment-specific tasks. Additionally, we demonstrate the existence of manually curated (deployment-specific) expert subsets that outperform training on all available data with accuracy gains up to 51.3 percent. Our benchmark highlights the critical role of tailored dataset curation in enhancing performance and training efficiency on deployment-specific distributions, which we posit will only become more important as global, public datasets become available across domains and ML models are deployed in the real world.

Problem

• Selects training subsets to optimize deployment-specific ML performance
• Addresses imbalanced data distributions in specialized real-world applications
• Benchmarks methods for deployment-focused dataset curation and efficiency

Innovation

• Selects deployment-specialized subsets from training data
• Introduces DataS^3 dataset and benchmark for DS3
• Demonstrates expert subsets outperform full training data