\section{Conclusion}
We formally define the problem of learning from label aggregates where source data has instance wise labels while target data has aggregate labels of instances grouped into bags. We also give bag-to-instance generalization error bound for regression tasks in LLP and use it to arrive at BagCSI loss. We propose two new methods, BL-WFA (based on BagCSI) and PL-WFA (based on a variant of BagCSI) that naturally incorporate the knowledge of aggregate labels from target domain in the domain adaptation framework leading to improvement over baseline methods. We also adapt several methods from literature in domain adaptation and LLP to this setting. Through experiments on synthetic and real-world datasets we show that our methods consistently outperform baseline techniques.
