Recommender systems have become increasingly accurate in suggesting content to users,
resulting in users primarily consuming content through recommendations. This can cause …

We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in
learning deep neural networks for several real and synthetic classification tasks. We show …

Deep neural networks (DNNs) have been applied to a variety of regulatory genomics tasks.
For interpretability, attribution methods are employed to provide importance scores for each …

In this paper we study the problem of robust influence maximization in the independent cascade
model under a hyperparametric assumption. In social networks users influence and are …

We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in
learning deep neural networks for several real and synthetic classification tasks. We show …

In this paper we advocate for a hyperparametric approach to learn diffusion in the independent
cascade (IC) model. The sample complexity of this model is a function of the number of …

Large online services employ thousands of people to label content for applications such as
video understanding, natural language processing, and content policy enforcement. While …

We investigate how and to which extent one can exploit risk-aversion and modify the perceived
cost of the players in selfish routing so that the Price of Anarchy ( $$\mathrm {PoA}$$ ) is …

The purpose of this paper is to investigate the existence of positive or negative causality
among variables that are part of the economic competitiveness methodology used by World …

Merging or acquiring has been a tactical practice for companies in order to penetrate markets.
As a means of foreign direct investment, it provides plenty of comparative advantages …