We develop learning-augmented algorithms for vertex-weighted and unweighted online bipartite fractional matching, with application to AdWords, and establish new robustness-consistency tradeoffs.

We propose an online portfolio selection algorithm that performs nearly optimal under accurate forecasts and remain provably robust.