LiveVal: Time-aware Data Valuation via Adaptive Reference Points
This work addresses the need for efficient data valuation in machine learning to prevent wasted computation, though it appears incremental as it builds on existing valuation methods with specific optimizations.
The paper tackles the problem of time-aware data valuation to enhance training efficiency and model robustness by proposing LiveVal, which achieves a 180x speedup over traditional methods while maintaining robust detection performance.
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.