Brandon Stewart

Agnieszka Dutkowska-Zuk, Austin Hounsel, Andre Xiong et al.

We study how and why university students chose and use VPNs, and whether they are aware of the security and privacy risks that VPNs pose. To answer these questions, we conducted 32 in-person interviews and a survey with 349 respondents, all university students in the United States. We find students are mostly concerned with access to content and privacy concerns were often secondary. They made tradeoffs to achieve a particular goal, such as using a free commercial VPN that may collect their online activities to access an online service in a geographic area. Many users expected that their VPNs were collecting data about them, although they did not understand how VPNs work. We conclude with a discussion of ways to help users make choices about VPNs.

Mikhail Khodak, Nikunj Saunshi, Yingyu Liang et al.

Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.