Seminars and Events
Optimal Structure and Parameter Learning of Ising models and Calibration of the D-Wave Quantum Computer
Event Details
Reconstruction of structure and parameters of a graphical model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards developing universal reconstruction algorithms which are both computationally efficient and require the minimal amount of expensive data. In this talk, we introduce a new method, Interaction Screening, which accurately estimates the model parameters using local optimization problems. We provide mathematical guarantees that the algorithm achieves perfect graph structure recovery with a near information-theoretically optimal number of samples and outperforms state of the art techniques, especially in the low-temperature regime which is known to be the hardest for learning. As an application, we show how the method can be used for correction of persistent biases and noise in the D-Wave quantum computer.