In various domains, different subjects may exhibit different responses to the same set of treatments. The exploration of this heterogeneity in the effects resulting from exposure has gained substantial interest in recent years. For instance, inferring the heterogeneous effect of a medical treatment on clinical outcome can contribute to the development of personalized treatment (Cai et al., 2011). A similar concept has found application in personalized marketing as well (Chandra et al., 2022).

We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism in terms of sequentially narrowing the gap between an upper and lower bound on the query.

Recently, contrastive multi-view clustering (MvC) has emerged as a promising avenue for analyzing data from heterogeneous sources, typically leveraging the off-the-shelf instances as positives and randomly sampled ones as negatives. In practice, however, this paradigm would unavoidably suffer from the Dual Noisy Correspondence (DNC) problem, where noise compromises the constructions of both positive and negative pairs.

The Rashomon effect is a mixed blessing in responsible machine learning.