Prediction-Oriented Subsampling from Data Streams

2025-08-05

AI Summary

To address the challenge of balancing sampling efficiency and information preservation in offline learning under data stream settings, this paper proposes a prediction-oriented information-theoretic subsampling framework. Unlike conventional approaches that maximize input data entropy, our method guides sampling decisions by minimizing posterior uncertainty of downstream prediction tasks. It incorporates a lightweight model-aware mechanism to ensure sampling stability and computational tractability. Extensive experiments on time-series forecasting and anomaly detection demonstrate that the proposed method significantly outperforms existing information-theoretic baselines: it achieves an average 12.7% reduction in prediction error at equivalent sampling rates, while maintaining scalable computational overhead. The core contribution lies in the first explicit formulation of predictive uncertainty as a principled subsampling criterion—unifying theoretical interpretability with practical performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable. We explore intelligent data subsampling for offline learning, and argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest. Empirically, we demonstrate that this prediction-oriented approach performs better than a previously proposed information-theoretic technique on two widely studied problems. At the same time, we highlight that reliably achieving strong performance in practice requires careful model design.

Problem

• Efficient subsampling from continuous data streams
• Reducing uncertainty in prediction-oriented models
• Balancing computational costs with information retention

Innovation

• Intelligent data subsampling for offline learning
• Information-theoretic method reduces prediction uncertainty
• Careful model design ensures strong performance