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.
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.
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...
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.
We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries.
To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.
Empirically, we demonstrate not only that qNEHVI is substantially more robust to observation noise than existing MOBO approaches, but also that it achieves state-of-the-art optimization performance and competitive wall-times in large-batch environments.
