  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 are still lacking 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 compared to alternative
approaches such as maximization of conditional likelihood, while considerably
improving upon the algorithm's run-time.
