We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content’s popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth.
We present Theodon, a hierarchical nonparametric Bayesian model, developed and deployed at Meta, that captures both the prevalence of label categories and the accuracy of...
In this work, we propose the first multi-objective BO method that is robust to input noise. We formalize our goal as optimizing the multivariate value-at-risk (MVAR), a risk...
Through a longitudinal analysis of 233,402 Facebook Groups, we examined 1) the factors that led to a community adopting post approvals and 2) how the setting shaped subsequent...
In this paper, we consider the moderating effect of people’s mindsets about social media — whether they think a platform is good or bad for themselves and for society more generally.
We introduce a novel regularizer which can describe those distributional preferences, while keeping the problem tractable. We show that this regularizer can be integrated into an...
This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting [9]) with greedy or optimal allocation combined with generalized...
We consider privacy in the context of streaming algorithms for cardinality estimation. We show that a large class of algorithms all satisfy epsilon-differential privacy, so long as...
We demonstrate a clear benefit to using this new class of acquisition functions on benchmark problems, and on a challenging real-world task of estimating a high-dimensional...
In this paper we tackle the problem, which is also known as profile inference and profile rectification. We investigate the classical approach for profile inference, based on computing minimum-cost maximum flows in a control-flow graph, and develop an extended model capturing the desired properties of real-world profiles.
