classical algorithms typically provide "one size fits all" performance, and do not leverage properties or patterns in their inputs. a recent line of work aims to address this issue by developing algorithms that use machine learning predictions to improve their performance. in this talk i will present two examples of this type, in the context of streaming and sampling algorithms. in particular, i will show how to use machine learning predictions to improve the performance of (a) low-memory streaming algorithms for frequency estimation (iclr’19), and (b) sampling algorithms for estimating the support size of a distribution (iclr’21). both algorithms use an ml-based predictor that, given a data item, estimates the number of times the item occurs in the input data set. \\ \\ the talk will cover material from papers co-authored with t eden, cy hsu, d katabi, s narayanan, r rubinfeld, s silwal, t wagner and a vakilian.