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.

[EX Q]: Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.
[EX A]: Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

[EX Q]: We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d. samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can then be used to specify sample complexity lower-bounds. Presence of these structural properties makes the graph class hard to learn. We derive corollaries of our main result that not only recover existing recent results, but also provide lower bounds for novel graph classes not considered previously. We also extend our framework to the random graph setting and derive corollaries for Erdős-Rényi graphs in a certain dense setting.
[EX A]: On the Information Theoretic Limits of Learning Ising Models

[EX Q]: <lb>Ordinal peer grading has been proposed as a simple and scalable solution for com-<lb>puting reliable information about student performance in massive open online courses.<lb>The idea is to outsource the grading task to the students themselves as follows. After<lb>the end of an exam, each student is asked to rank —in terms of quality— a bundle of<lb>exam papers by fellow students. An aggregation rule will then combine the individ-<lb>ual rankings into a global one that contains all students. We define a broad class of<lb>simple aggregation rules and present a theoretical framework for assessing their ef-<lb>fectiveness. When statistical information about the grading behaviour of students is<lb>available, the framework can be used to compute the optimal rule from this class with<lb>respect to a series of performance objectives. For example, a natural rule known as<lb>Borda is proved to be optimal when students grade correctly. In addition, we present<lb>extensive simulations and a field experiment that validate our theory and prove it to<lb>be extremely accurate in predicting the performance of aggregation rules even when<lb>only rough information about grading behaviour is available.
[EX A]:
How effective can simple ordinal peer grading be?∗