The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group


with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Statistical Filtering for Optimization over Expectation Operators

Vivak Patel (UW),

Date and Time: Nov 21, 2018 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


The problem of optimizing objective functions that involve expectation or integral operators is common in a number of fields. This problem is commonly addressed using one of three frameworks or hybrids thereof: Sample Average Approximation/Monte Carlo (SAA/MC), Bayesian Optimization (BO), and Stochastic Approximation (SA). While the methods that belong to such frameworks have well-controlled theoretical properties and certain desirable computational properties, they often have certain crippling practical challenges. For example, in the SAA/MC and BO paradigms, we can generate increasingly accurate estimates of the objective function and its derivatives, which is valuable in the evaluation of iterates and hyperparameters, yet this comes at an increasing cost. On the other hand, in the SA paradigm, we can handle increasing amounts of data with a fixed per iteration cost, yet we lose the ability to evaluate iterates and hyperparameters, which can result in painfully slow convergence or exponential divergence. In this talk, we will introduce a new paradigm to achieve the best of both worlds: we will generate increasingly accurate estimates of the objective and its derivatives with a fixed per iteration cost. We will demonstrate its potential on three problems coming from statistics, machine learning, and stochastic optimal control.