CS 499/579 :: Spring 2023 :: Trustworthy Machine Learning



[Mandatory] Written Paper Critiques

[Due] Before each class.
(e.g., the deadline for Tuesday's class papers is 9:59:59 AM on Tuesday.)

The required readings are posted on the course syllabus, and you need to write a critique for one out of the 2-3 papers per class. In the syllabus, you sometimes find [Bonus] papers, but you shouldn't choose them for this. Keep in mind that this is not light pleasure reading. You may require to read a few more relevant papers to understand the reading materials. After you are used to this reading, it may take 2-3 hours to read each paper.

Your critique for each paper MUST have the following fields:

Please make your best effort to write concise and precise statements. For example:

In your critiques, I will look for evidence that you thought about the problem discussed in the paper; you incorporated personal experience, you looked for how the security issues of machine learning discussed in the paper are addressed today, and so on. Think critically about the paper and do not accept the authors' opinions as-is. It is the authors' job to convince you!

Here, I also share a sample critique of mine for reference [example].

Grading Policy
Submission Instructions

Upload your critique to Canvas.


[Mandatory] In-Class Paper Presentation and Discussion

[Note] You can pick any paper relevant to the topics for the day you present.

You also need to sign-up for the in-class paper presentation and discussion. You only need to sign-up once throughout the term. You will require to present a paper as if the paper is yours and lead the discussion during the lecture.

Please use this Google Sheet [link] to sign-up (you need an ONID to access this sheet); max. 2 students can sign-up for each presentation. You MUST sign-up at least one week before the class you are willing to present. You MUST meet Sanghyun ONCE—0.5 weeks before the class for revising your presentation.

Your presentation and discussion will be tentatively 35-45 minutes.

Grading Policy