  We address the problem of learning of continuous exponential family
distributions with unbounded support. While a lot of progress has been made on
learning of Gaussian graphical models, we still lack scalable algorithms for
reconstructing general continuous exponential families modeling higher-order
moments of the data beyond the mean and the covariance. Here, we introduce a
computationally efficient method for learning continuous graphical models based
on the Interaction Screening approach. Through a series of numerical
experiments, we show that our estimator maintains similar requirements in terms
of accuracy and sample complexity scalings compared to alternative approaches
such as maximization of conditional likelihood, while considerably improving
upon the algorithm's run-time.
