[Research] After a long break, Auton Lab meetings are back!

[Artur Dubrawski]

Here is an announcement of the next, very exciting one:

Time & Place: Tuesday, May 15th, at 11:30am, NSH 1507

Food: Yes

Speaker: Brent Bryan

Title & Abstract:

Efficiently Computing Minimax Expected-Size Confidence Regions

Given observed data and a collection of parameterized candidate
models, a 1-alpha confidence region in parameter space provides useful
insight as to those models which are a good fit to the data, all while
keeping the probability of incorrect exclusion below alpha. With
complex models, optimally precise procedures (those with small
expected size) are, in practice, difficult to derive; one solution is
the Minimax Expected-Size (MES) confidence procedure. The key
computational problem of MES is computing a minimax equilibria to a
certain zero-sum game. We show that this game is convex with bilinear
payoffs, allowing us to apply any convex game solver, including linear
programming.  Exploiting the sparsity of the matrix, along with using
fast linear programming software, allows us to compute approximate
minimax expected-size confidence regions orders of magnitude faster
than previously published methods. We test these approaches by
estimating parameters for a cosmological model.

A copy of the paper can be found on the autonlab.org website.