Discrete Optimization and Machine Learning

Sebastian Pokutta (WiSe 2024 / Seminar)

Both Machine Learning methods as well as Discrete Optimization methods are important tools in many real-world applications. In this course we will primarily study the interplay of integer programming and, more broadly, discrete optimization methods and machine learning.
The format of the course is research-oriented requiring active participation of the students. The course will be a mix of student presentation, discussions, and project work. In the first few weeks in-class projects will be defined. 

Course organization

Prerequisites: Linear Algebra, Analysis, and Discrete Optimization (ADM I/ADM II) 

Registration: Together with paper selection after first meeting (seminar outline)


Participation requirements:
Students are expected to individually 


Paper/project selection:
Students choose one of the papers below to work on


Contribution:
Every student is expected to make a contribution to the given topic and not just reproduce the results. The aim is not to obtain  groundbreaking new results, but to get an impression of scientific work. Furthermore, you also need to have a very sound understanding of the subject in order to contribute something yourself. Contributions can be an extension of the original algorithm, a new theoretical proof or a comparison with new research.

Examples for strong contributions:

COIL: A Deep Architecture for Column Generation

Sparse Adversarial and Interpretable Attack Framework

PEP: Parameter Ensembles by Perturbation


Timeline:

Reading material by topic (red papers are no longer available):