Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: <lb>Many machine learning approaches are characterized by information constraints on how<lb>they interact with the training data. These include memory and sequential access constraints<lb>(e.g. fast first-order methods to solve stochastic optimization problems); communication con-<lb>straints (e.g. distributed learning); partial access to the underlying data (e.g. missing features<lb>and multi-armed bandits) and more. However, currently we have little understanding how such<lb>information constraints fundamentally affect our performance, independent of the learning prob-<lb>lem semantics. For example, are there learning problems where any algorithm which has small<lb>memory footprint (or can use any bounded number of bits from each example, or has certain<lb>communication constraints) will perform worse than what is possible without such constraints?<lb>In this paper, we describe how a single set of results implies positive answers to the above, for<lb>a variety of settings.

Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation