Meta Research

We show the first non-trivial approximation factor for this problem by giving a polynomial time O(log k)-approximation algorithm for graphs with treewidth k.

To address this issue, we propose using probabilistic reparameterization (PR). Instead of directly optimizing the AF over the search space containing discrete parameters...

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...