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Proposes Trans-Ising, a transfer learning method for high-dimensional Ising models that uses a loss-based source screening rule and two-stage estimation to improve estimation accuracy over target-only and naive pooling methods.
This paper presents a polynomial-time algorithm for learning the structure of a Gaussian graphical model from a single trajectory of Glauber dynamics, with a trajectory-length guarantee that does not depend on the mixing time.