Chris Maddison, University of Toronto
Mon 11 May 2015, 11:00 - 12:00
IF 4.31/4.33

If you have a question about this talk, please contact: Mary-Clare Mackay (mmackay3)

THIS SEMINAR IS TAKING PLACE ON A MONDAY

The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we

show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the

method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new

construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel

process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more

efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.

http://www.cs.toronto.edu/~cmaddis/