The proliferation of services and service interactions within microservices and cloud-native
applications, makes it harder to detect failures and to identify their possible root causes …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

Stochastic differential equations are differential equations whose solutions are stochastic
processes. They exhibit appealing mathematical properties that are useful in modeling …

Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

When data are not normally distributed, researchers are often uncertain whether it is
legitimate to use tests that assume Gaussian errors, or whether one has to either model a …

Background Brain activation in response to spoken motor commands can be detected by
electroencephalography (EEG) in clinically unresponsive patients. The prevalence and …

Network approaches to psychometric constructs, in which constructs are modeled in terms of
interactions between their constituent factors, have rapidly gained popularity in psychology …

This is your last chance. After this, there is no turning back. You take the blue pill-the story
ends, you wake up in your bed and believe whatever you want to believe. You take the red …

Learning models with uniform performance via distributionally robust optimization Page 1 The
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …