DISC Learns to Combine Time-Series Dataset Similarity Metrics for Transfer Prediction
Yen-Ku Liu · hf · 2026-10-26
A paper from Adobe Research, Dolby and NYCU proposes DISC (Domain-Integrated Similarity Cost), a learnable framework for time-series dataset similarity.
- Problem: existing similarity metrics capture different dataset relationships and vary in how well they align with downstream utility
- DISC uses calibration transfer observations to select, weight and combine similarity metrics per downstream task
- After calibration, it scores source-target pairs using only their similarity-metric values, better predicting forecasting transfer performance
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