EstImAgg: A Learning Framework for Groupwise Aggregated Data

Avradeep Bhowmik, Minmin Chen, Zhengming Xing, Suju Rajan

Abstract: Aggregation is a common technique in data-driven applications for handling issues like privacy, scalability and reliability in a vast range of domains including healthcare, sensor networks and web applications. However, despite the ubiquitousness, extending machine learning methods to the aggregation context is unfortunately not well-studied. In this work, we consider the problem of learning individual level predictive models when the target variables used for training are only available as aggregates. In particular, this problem is a critical bottleneck in designing effective bidding strategies in the context of online advertising where ground-truth cost-per-click (CPC) data is aggregated before being released to advertisers. We introduce a novel learning framework that can use aggregates computed at varying levels of granularity for building individual-level predictive models. We generalise our modelling and algorithmic framework to handle data from diverse domains, and extend our techniques to cover arbitrary aggregation paradigms like sliding windows and overlapping/non-uniform aggregation. We show empirical evidence for the efficacy of our techniques with experiments on both synthetic data and real data from the online advertising domain as well as healthcare to demonstrate the wider applicability of our framework.