Average-case complexity in statistical inference: A puzzle-driven research seminar

TL;DR

This paper discusses a student seminar on average-case complexity in statistical inference, utilizing an active 'jigsaw' learning format to enhance engagement, understanding, and participation among students.

Contribution

It introduces and evaluates a novel active learning approach for teaching complex topics in statistical inference and complexity theory.

Findings

Increased student engagement and participation.

More accessible and understandable student presentations.

Provision of exercise sheets for educational use.

Abstract

These notes describe our experience with running a student seminar on average-case complexity in statistical inference using the jigsaw learning format at ETH Zurich in Fall of 2024. The jigsaw learning technique is an active learning technique where students work in groups on independent parts of the task and then reassemble the groups to combine all the parts together. We implemented this technique for the proofs of various recent research developments, combined with a presentation by one of the students in the beginning of the session. We describe our experience and thoughts on such a format applied in a student research seminar: including, but not limited to, higher engagement, more accessible talks by the students, and increased student participation in discussions. In the Appendix, we include all the exercises sheets for the topic, which may be of independent interest for courses on statistical-to-computational gaps and average-case complexity.