Linear Regression using Heterogeneous Data Batches Ayush Jain

In many learning applications, data are collected from multiple sources, each providing a batch of samples that by itself is insufficient to learn its input-output relationship. A common approach assumes that the sources fall in one of several unknown subgroups, each with an unknown input distribution and input-output relationship. We consider one of this setup's most fundamental and important manifestations where the output is a noisy linear combination of the inputs, and there are k subgroups, each with its own regression vector.