In this paper, we present CLARA (Confidence of Labels and Raters), a system developed and deployed at Facebook for aggregating reviewer decisions and estimating their uncertainty. We perform extensive validations and describe the deployment of CLARA for measuring the base rate of policy violations, quantifying reviewers’ performance, and improving their efficiency.
We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments.
Since many problems are efficiently solved using specific operators, Nevergrad therefore now enables using specific operators within generic algorithms: the underlying structure of the problem is user-defined information that several families of optimization methods can use and benefit upon. We explain how this API can help analyze optimization methods and how to use it for the optimization of a structured Photonics physical testbed, and show that this can produce significant improvements.
It is widely assumed that firms experiment with their online advertising to identify more profitable approaches to then increase their investment in more profitable advertising, increasing their overall performance. Generalizable evidence on the actual use of such experiment-based learning by firms is sparse. The study herein addresses this shortcoming – detailing the extent to which large advertisers are utilizing experimentation along with evidence on the benefits of doing so.
To better test the potential causal pathways between trust and behaviors or group properties, we paired a two-wave longitudinal survey of 2358 participants in Facebook Groups with logged activity on Facebook. Using latent change score modeling, we examined how trust may predict changes in behavior or group properties and how behaviors and group properties may predict changes in trust.