Crowdsourced Knowledge

Iterative Classroom Teaching

Abstract We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using $\mathcal{O} \left(\min\left\{d,N\right\} \log \frac{1}{\epsilon}\right)$ examples, where $d$ is the ambient dimension of the problem, $N$ is the number of learners, and $\epsilon$ is the accuracy parameter.

Iterative Classroom Teaching

Abstract We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using $\mathcal{O} \left(\min\left\{d,N\right\} \log \frac{1}{\epsilon}\right)$ examples, where $d$ is the ambient dimension of the problem, $N$ is the number of learners, and $\epsilon$ is the accuracy parameter.

Fake News Detection in Social Networks via Crowd Signals

Abstract Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users’ flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize \emph{the spread of misinformation} by stopping the propagation of fake news in the network.