 Northwestern University
In finance and insurance, simulations are often run repeatedly with different inputs. For example, in a simulation for risk management, the simulation model for valuing derivative securities is run many times with different macroeconomic conditions. We present a new concept of green simulation, which seeks to increase the computational efficiency of the current experiment by reusing simulation output generated during previous experiments. Green simulation views simulation output as scarce resource and turns the computational expense in an experiment into computation investment for future ones. We propose and examine two green simulation estimators for repeated experiments whose inputs are observations from an underlying stochastic process. Two types of convergence are shown for these green simulation estimators under different ass
umptions. As illustrated by two practical applications: catastrophe bond pricing and periodic credit risk evaluation, green simulation is both theoretically sound and practically useful.
INRIA

Machine learning has grown significantly in the last two decades and have had impact in diverse areas such as computer vision, natural language processing, computational biology and social sciences. These new applications have made apparent though that real world data have a richer structure than have been modeled by some of the classical paradigms of machine learning such as binary classification and regression. In machine translation for example, the algorithm needs to choose amongst an exponential number of possible sequences of words as translations, and not just a few options as in handwritten digits recognition. A key challenge in modern machine learning is to find ways to model this complex structure in a scalable manner which is still robust to model misspecification. In this talk, I will present such a method that can exploit the combinatorial structure in data represented by graphs, with various applications such as the task of word alignment in natural language processing, the alignment of large knowledge bases for the Semantic Web or the tracking of multiple objects in video. I will also present how these problems have motivated progress on novel optimization techniques including improvements on the venerable Frank-Wolfe optimization algorithm (1956) or Robbins-Monroe stochastic gradient method (1951). These examples will highlight how the rich two-way street between optimization and machine learning enables us to exploit more effectively the structure of complex data.