Recommendation algorithms that select the most relevant item for sequentially arriving users or queries have become vital for navigating the internet and its many online platforms.

We study learning problems on correlated stochastic block models with two balanced communities.

Specifically, LocCa employs two tasks, bounding box prediction and location-dependent captioning, conditioned on the image pixel input.