- The session of Monday 24.07.17 will take place on Tuesday 25.07.17 at 13:00 in room 02.11.058. All other sessions will take place according to the regular schedule.
- Slides with organizational updates can be found here.
Machine learning algorithms are getting a wide adoption across numerous domains of human activity. They are responsible for tasks ranging from content recommendation on the web to trading in the stock markets. At the same time, in many real-world scenarios the data contains imperfections that hinder the performance of these algorithms. For instance, in the industrial setting networks of sensors are prone to noise and random failures. On the internet, e-commerce platforms and social networks are subject to adversarial attacks by spammers and fraudsters. Such scenarios require novel data mining algorithms that are robust and immune to corruptions in the data.
The goal of the seminar is to familiarize the students with the state of the art in design of robust data mining algorithms. Topics discussed include both the extensions of classic machine learning algorithms aimed to increase robustness (e.g. PCA, spectral clustering), as well as high-level ideas surrounding the subject (e.g. differential privacy).
|Date||Topic||Student||Supervisor||References||Reviewer 1||Reviewer 2|
*Peter J. Rousseeuw, Annick M. Leroy - Robust Regression and Outlier Detection
|15.05||Robust Matrix Factorization||Maida||Aleksandar||Boonyakorn||Nikolai|
|29.05||Robust Community Detection||Stevica||Aleksandar||Nikolai||Csongor|
|12.06||Robust Time Series / Sequence Modeling||Lorenzo||Oleksandr||Thomas||Daniela|
|19.06||Attacks on Classifiers||Viet||Aleksandar||Yuesong||Stevica|
|26.06||Fooling Deep Networks||James||Aleksandar||Yuesong||Maida|
|03.07||Learning in the Adversarial Setting||Daniela||Aleksandar||Boonyakorn||Csongor|
|10.07||Learning from Crowds||Yuesong||Oleksandr||Alexander||Lorenzo|
|24.07||Robustness of Complex Networks||Alexander||Oleksandr||Network Robustness||Stevica||Lorenzo|
* hardcopy of Peter J. Rousseeuw, Annick M. Leroy - Robust Regression and Outlier Detection is available in the TUM library.
Also available via Eaccess http://onlinelibrary.wiley.com.eaccess.ub.tum.de/book/10.1002/0471725382
** use Eaccess to access the PDFs of the chapters, i.e. onlinelibrary.wiley.com.eaccess.ub.tum.de/book/10.1002/0470010940
- 12 Participants
- 5 ETCS
- Language: English
- Weekly meetings every Monday 14:30-16:00, room 02.09.14.
- Please send your questions regarding the seminar to firstname.lastname@example.org.
- The seminar is intended for master students of the Computer Science department.
- This seminar deals with advanced and cutting edge topics in machine learning and data mining research. Therefore, the students are expected to have a solid background in these areas (e.g. having attended at least one of the related lectures, such as "Mining Massive Datasets", "Machine Learning", etc.).
- Extended abstract: 1 page article document class with motivation, key concepts and results.
- Paper: 5-8 pages in ACM format.
- Presentation: 30 minutes talk + 15 minutes discussion. (Optional: Beamer template)
- Peer-review process.
- Mandatory attendance of the weekly sessions.
- 27.01.2017 17:00: Pre-course meeting in Interims Hörsaal 2. Slides can be found here.
- 03.02.17 - 08.02.17: Application and registration in the matching system of the department
- After 15.02.17: Notification of participants
- 01.03.2017 11:00: Kick-off meeting in the room 02.09.014. Slides can be found here.
- Starting 24.04.17: Weekly meetings every Monday 14:30-16:00, room 02.09.14
- 1 week before the talk: submission of an extended abstract and slides
- One day before the talk: submission of a preliminary paper for review
- 1 week after the talk: receiving comments from reviewers
- 2 week after the talk: submission of the final paper