Seminar on Theoretical Aspects of Machine Learning Algorithms

General information

Format

This seminar simulates a machine learning conference, where the students take on the role of authors and reviewers. It consists of multiple phases.

1. Proposal phase

Attend the mandatory first meeting on 07.03, 15:00 (either in person at Gußhausstraße 27-29 CA 03 13, or remotely at https://tuwien.zoom.us/my/maxthiessen).

Option 1: our suggestions

You select two projects/papers (i.e., two bullet points) from one of the topics below. You will work with the material mentioned in the overview and the project-specific resources.

Option 2: your own projects

You choose two different own project ideas to work on. This can be some existing machine learning paper/work or an own creative idea in the context of machine learning. Importantly, it has to be specific and worked out well.

Independent of the option you chose, understand the fundamentals of your projects and try to answer the following questions:

Select projects and write a short description of them together with the answers to the questions (~3 sentences shoud be sufficient) in TUWEL.

We can only accept your own proposals if you can answer the mentioned questions and have a well worked out project idea.

2. Bidding and assignment phase

You will also act as reviewers and bid on the projects of your peers you want to review. Based on the biddings, we (in the role as chairs of the conference) will select one of each student’s proposals as the actual project you will work on for the rest of this semester. You do not need to work on the other project, anymore. Additionally, we will also assign two different projects from other students to you, which you will have to review later in the semester.

3. Working phase

Now the actual work starts. Gather deep understanding of your project, write a first draft of your report and give a 5-minute presentation. We recommend to go beyond the given material.

4. Reviewing phase

You will again act as a reviewer for the conference by writing two reviews, one for each draft report assigned to you.

5. Writing phase

Based on the reviews from your peers (and our feedback) you will further work on your project.

6. Submission phase

Give a final presentation and submit your report.

General resources (freely available books and lecture notes)

Topics (Tentative)

You should have access to the literature and papers through Google scholar, DBLP, the provided links, or the TU library.

Knowledge Graph Embeddings (click to expand)

Overview:

Papers and projects:

Neurosymbolic AI / Logic & ML (click to expand)

Overview:

Papers and projects:

Submodularity in machine learning (click to expand)

Overview:

Papers and projects:

Graph kernels (click to expand)

Overview:

Papers and topics:

Kernel methods (click to expand)

Overview:

Papers and projects:

Semi-supervised learning (click to expand)

Overview:

Papers and projects:

Active learning (click to expand)

Overview:

Papers and projects:

Online learning (click to expand)

Overview:

Papers and projects:

Clustering and dimensionality reduction (click to expand)

Overview:

Papers and projects:

Modern aspects of learning theory (click to expand)

Overview:

Papers and projects:

Explainable AI (click to expand)

Overview:

Papers and projects:

Differential privacy (click to expand)

Overview:

Papers and projects:

Neuro-inspired DL (click to expand)

Overview:

Papers and projects:

Optimization (and Generalization) in Neural Networks (click to expand)

Overview:

Papers and projects: