Monte Carlo Methods in Stochastic Convex Optimization
University of Vienna
Wednesday, Nov 10, 2020
12:00 - 1:00 PM
We develop a novel procedure for estimating the optimizer of general convex stochastic optimization problems from an iid sample. This procedure is the first one that exhibits the optimal statistical performance in heavy tailed situations and also applies in high dimensional settings. We discuss the results at hand of the portfolio optimization problem. Joint work with Shahar Mendelson.
Operations Research Colloquia: http://or.stanford.edu/oras_seminars.html