Optimal structure and parameter learning of Ising models and calibration of the D-Wave quantum computer

Friday, January 27, 2017, 11:00 am - 12:00 pm PSTiCal
11th Flr Conf Room-CR #1135
This event is open to the public.
AI Seminar
Andrey Lokhov (Los Alamos National Lab)




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.


Currently Postdoctoral Research Assistant at Los Alamos National Laboratory (Theoretical Division and Center for Nonlinear Studies). Working on statistical physics and machine learning.

Ph.D. (2014) Physics, Laboratoire de Physique Théorique et Modèles Statistiques (LPTMS), Université Paris-Sud (University Paris 11), France

M.Sc. (2011) Theoretical Physics, Ecole Normale Superieure (ENS), Paris, France

M.Sc. (2011) Theoretical Physics, Novosibirsk State University, Novosibirsk, Russia

B.Sc. (2009) Physics, Ecole Polytechnique, Paris, France 

« Return to Upcoming Events