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Rethinking Representativeness and Diversity in Dynamic Data Selection

arXiv:2603.04981v1 Announce Type: new Abstract: Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: representativeness and diversity. Instead of local geometric centrality, we define representativeness as coverage of dataset-level common or high-frequency feature factors. Instead of within-subset dispersion, we define diversity at the process level, requiring the selection trajectory to gradually include complementary rare factors over training. Based on this view, we propose a dynamic selection framework with three components. First, we score representativeness in a plug-in feature space to prioritize samples covering frequent factors. We instantiate this with a sparse autoencoder trained on the target dataset, using sparse unit activations to summarize both individual samples and dataset-wide factor statistics. Second, we realize process-level diversity by combin

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Yuzhe Zhou, Zhenglin Hua, Haiyun Guo, Yuheng Jia · · 1 min read · 2 views
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arXiv:2603.04981v1 Announce Type: new Abstract: Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: representativeness and diversity. Instead of local geometric centrality, we define representativeness as coverage of dataset-level common or high-frequency feature factors. Instead of within-subset dispersion, we define diversity at the process level, requiring the selection trajectory to gradually include complementary rare factors over training. Based on this view, we propose a dynamic selection framework with three components. First, we score representativeness in a plug-in feature space to prioritize samples covering frequent factors. We instantiate this with a sparse autoencoder trained on the target dataset, using sparse unit activations to summarize both individual samples and dataset-wide factor statistics. Second, we realize process-level diversity by combining rare-factor sampling with a Usage-Frequency Penalty that promotes sample rotation, provably discourages monopoly, and reduces gradient bias. Third, we couple the two-dimensional scoring with a smooth scheduler that transitions selection from core-pattern consolidation to rare-factor exploration, without extra gradients, influence estimates, or second-order computations on the training model. Extensive experiments on five benchmarks across vision and text tasks demonstrate improved accuracy-efficiency trade-offs across models. Our method matches or exceeds full-data accuracy with over 2x training acceleration. Code will be released.

Executive Summary

This article proposes a novel approach to dynamic data selection in machine learning, focusing on representativeness and diversity. The authors redefine these concepts by prioritizing coverage of common or high-frequency feature factors and gradual inclusion of complementary rare factors over training. Their framework consists of three components: a representativeness scorer based on sparse autoencoder activations, a process-level diversity mechanism leveraging rare-factor sampling and Usage-Frequency Penalty, and a smooth scheduler for transitioning between core-pattern consolidation and rare-factor exploration. Experimental results demonstrate improved accuracy-efficiency trade-offs across five benchmarks, achieving over 2x training acceleration with comparable accuracy to full-data training. This work has significant implications for accelerating training in deep learning, particularly in scenarios with limited computational resources.

Key Points

  • ▸ Rethinking representativeness and diversity in dynamic data selection
  • ▸ Defining representativeness as coverage of common or high-frequency feature factors
  • ▸ Introducing process-level diversity through rare-factor sampling and Usage-Frequency Penalty
  • ▸ Proposing a smooth scheduler for transitioning between core-pattern consolidation and rare-factor exploration

Merits

Strength in mathematical formulation

The authors provide a clear and rigorous mathematical formulation of their framework, making it accessible and applicable to a broad range of machine learning practitioners.

Empirical validation

The experimental results demonstrate the effectiveness of the proposed framework, showcasing improved accuracy-efficiency trade-offs across multiple benchmarks.

Practical relevance

The framework's ability to accelerate training while preserving accuracy has significant practical implications, particularly in scenarios with limited computational resources.

Demerits

Limited generalizability

The framework's performance may not generalize to all types of datasets or tasks, requiring further evaluation and adaptation for specific applications.

Computational overhead

The introduction of a smooth scheduler and Usage-Frequency Penalty may incur additional computational overhead, potentially offsetting the benefits of accelerated training.

Expert Commentary

The article presents a significant contribution to the field of machine learning, offering a novel framework for dynamic data selection that addresses the limitations of existing approaches. The authors' emphasis on representativeness and diversity provides a more comprehensive understanding of data selection, enabling practitioners to develop more effective and efficient models. However, further research is necessary to fully generalize the framework's performance and mitigate potential computational overhead. The implications of this work extend beyond the realm of machine learning, with potential applications in fairness, accountability, and transparency in AI systems.

Recommendations

  • ✓ Future work should focus on adapting the proposed framework for diverse datasets and tasks, exploiting its strengths while mitigating potential limitations.
  • ✓ The authors should provide a more detailed analysis of the computational overhead associated with the smooth scheduler and Usage-Frequency Penalty, ensuring that the benefits of accelerated training are not offset by additional computational requirements.

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