We consider the problem of learning the underlying graph of an unknown Ising model on pspins from a collection of i.i.d.

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d.

The main effort is to improve upon the recent results provided by Bresler, showing that the complexity of identifying the structure of max degree d Ising model is polynomial in p and independent of d. Strong Points: 1) The timeliness of the topic in this paper is good, meaning that there is currently ongoing interest and work on Ising model reconstruction. Weak points: 1) The whole approach is based on the introduction of the ISO. This is the main trick in the proposed approach. Other than that, the rest of the method and its analysis are usual and well studied (l_1-penalization and connection with the tutorial by Negahban et.

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d.

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established information theoretic lower-bound. Our statistical estimator has a physical interpretation in terms of "interaction screening". The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established information theoretic lower-bound. Our statistical estimator has a physical interpretation in terms of "interaction screening". The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established information-theoretic lower-bound. Our statistical estimator has a physical interpretation in terms of "interaction screening". The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.