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

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

In this monograph, we have attempted to summarize the actively develo** field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

Regularization is a widely used technique throughout statistics, machine learning, and
applied mathematics. Modern applications in science and engineering lead to massive and …

Africa is often seen as a continent of mass migration and displacement caused by poverty,
violent conflict and environmental stress. Yet such perceptions are based on stereotypes …

A continuing mystery in understanding the empirical success of deep neural networks is
their ability to achieve zero training error and generalize well, even when the training data is …

We present a communication-efficient surrogate likelihood (CSL) framework for solving
distributed statistical inference problems. CSL provides a communication-efficient surrogate …

We consider the problem of training a $ d $ dimensional model with distributed differential
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …

On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …

Regularized estimation in sparse high-dimensional time series models Page 1 The Annals
of Statistics 2015, Vol. 43, No. 4, 1535–1567 DOI: 10.1214/15-AOS1315 © Institute of …