LiveVal: Time-aware Data Valuation via Adaptive Reference Points
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915152698277888 |
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| author | Xu, Jie Wu, Zihan Wang, Cong Jia, Xiaohua |
| author_facet | Xu, Jie Wu, Zihan Wang, Cong Jia, Xiaohua |
| contents | Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics.
We propose LiveVal, an efficient time-aware data valuation method with three key designs:
1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage.
We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiveVal: Time-aware Data Valuation via Adaptive Reference Points Xu, Jie Wu, Zihan Wang, Cong Jia, Xiaohua Machine Learning Artificial Intelligence Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient time-aware data valuation method with three key designs: 1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage. We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance. |
| title | LiveVal: Time-aware Data Valuation via Adaptive Reference Points |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10489 |