Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning

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 either in person or remotely (details on TUWEL).

Option 1: our suggestions

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

Option 2: your own idea + one of our suggestions

You choose your own topic to work on. This can be some existing machine learning paper/work or an own creative idea in the context of machine learning. We strongly encourage you to start from existing papers from the following venues: NeurIPS, ICML, ICLR, COLT, AISTATS, UAI, JMLR, MLJ. Importantly, your idea has to be specific and worked out well. Nevertheless, choose one of our suggestions as well.

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

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

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

2. Bidding and assignment phase

You will also act as reviewers and bid on the topics 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 topic you will work on for the rest of this semester. You do not need to work on the other topic, anymore. Additionally, we will also assign two different topics 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 topic, 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 topic.

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.

Learning Logically Definable Concepts (click to expand)
Active Learning (click to expand)
Equivariant Neural Networks (click to expand)
GNNs (click to expand)
Modern aspects of learning theory (click to expand)
Neurosymbolic AI / Logic & ML (click to expand)
Optimisation (and Generalisation) in Neural Networks (click to expand)
Trustworthy ML (click to expand)