We address this general problem in the context of image data (photos) by proposing which photos to archive to meet an online storage budget. The decision is based on factors such...
We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms...
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...
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...
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 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...
To address this problem, we develop Bayesian optimization with preference exploration, a novel framework that alternates between interactive real-time preference learning with...
