Post-ADC Inference: Valid Inference After Active Data Collection
The validity of statistical inference depends critically on how data are collected. When data gathered through active data collection (ADC) are reused for a post-hoc inferential task, conventional inference can fail because the sampling is adaptively biased toward regions favored by the collection strategy. This issue is especially pronounced in black-box optimization, where sequential model-based optimization (SMBO) methods such as the tree-structured Parzen estimator (TPE) and Gaussian process upper confidence bound (GP-UCB) preferentially concentrate evaluations in promising regions. We study statistical inference on actively collected data when the inferential target is constructed in...
| Authors | Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka et al. |
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| Primary category | stat.ML |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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