CIS 677 Topics in Algorithms
Algorithmic Decision Theory and Bayesian Optimization
Class location: Levine 612, TuTh 430-600pm.
Course Description: We will look at optimization algorithms for
problems with partial and incomplete information. These problems have a
longstanding history in statistics, control and optimization theory; however are
frequently computationally intractable. We will consider these problems through
the lens of Approximation Algorithms, and incorporate a variety of computational constraints and not just available information or running time.
Plan: I will be giving most of the lectures. The students will be
asked to form small groups of size two or three. Each group chooses a problem
in consultation with me (obviously with some connection to the course material, but not necessarily the exact same problems).
The group works on the problem throughout the semester and is graded together. The grade is the smallest component of this course - the goal of the
course is to learn these topics and explore possible avenues of research.
You can choose an experimental/exploratory project, but I would encourage
it to have a sizeable analysis component.
Ashish Goel is teaching a similar class in Stanford and
we will try to have coordinated lecture notes and supporting material.
Kamesh Munagala taught a similar
class at Gatech, and the reading list there would have overlap with this course.
Papers and Topics