Speaker
Description
Supervised learning under measurement constraints presents a challenge in the field of machine learning. In this scenario, while predictor observations are available, obtaining response observations is arduous or cost-prohibitive. Consequently, the optimal approach involves selecting a subset of predictor observations, acquiring the corresponding responses, and subsequently training a supervised learning model on this subset.
Among various subsampling techniques, the design-inspired subsampling methods have attracted great interest in recent years (see Yu et al. (2023) for a review). Most of these approaches have shown remarkable performance in coefficient estimation and model prediction, but their performance heavily relies on a specified model. When the model is misspecified, misleading results may be obtained.
In this work we provide a comparative analysis of methods that account for model misspecification, as for instance the LowCon approach introduced by Meng et al. (2021) for selecting a subsample using an orthogonal Latin hypercube design, or other subsampling criteria based on space-filling designs. Furthermore, the robustness of these methods to outliers is also assessed (see Deldossi et al. (2023)). Empirical comparisons are conducted, providing insights into the relative performance of these approaches.
References
Deldossi, L., Pesce, E., Tommasi, C. (2023) Accounting for outliers in optimal subsampling methods. Statistical Papers, 64(4), 1119-1135.
Meng, C., Xie, R., Mandal, A., Zhang, X., Zhong, W., Ma, P. (2021) Lowcon: a design-based subsampling approach in a misspecified linear model. Journal of Computational and Graphical Statistics, 30:694–708
Yu, J., Ai, M., Ye, Z. (2024) A review on design inspired subsampling for big data. Statistical Papers, 65, 467–510
| Type of presentation | Talk |
|---|---|
| Classification | Mainly methodology |
| Keywords | Massive data - Orthogonal design - Space-filling design |