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: Consider Input: Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.

Output: Deep Learning for User Comment Moderation


Input: Consider Input: Consider a weighted or unweighted k-nearest neighbor graph that has been built on n data points drawn randomly according to some density p on R. We study the convergence of the shortest path distance in such graphs as the sample size tends to infinity. We prove that for unweighted kNN graphs, this distance converges to an unpleasant distance function on the underlying space whose properties are detrimental to machine learning. We also study the behavior of the shortest path distance in weighted kNN graphs.

Output: Shortest path distance in random k-nearest neighbor graphs


Input: Consider Input: We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on the idea<lb>of predictable sequences. First, we recover theMirror Prox algorithm for offline optimization, prove an extension<lb>to Hölder-smooth functions, and apply the results to saddle-point type problems. Next, we prove that a version<lb>of Optimistic Mirror Descent (which has a close relation to the Exponential Weights algorithm) can be used by<lb>two strongly-uncoupled players in a finite zero-summatrix game to converge to the minimax equilibrium at the<lb>rate ofO((logT )/T ). This addresses a question of Daskalakis et al [6]. Further, we consider a partial information<lb>version of the problem. We then apply the results to convex programming and exhibit a simple algorithm for the<lb>approximateMax Flow problem.


Output: Optimization, Learning, and Games with Predictable Sequences