Seminar: Robust Data Mining Techniques


  • 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
24.04 Robust Regression Boonyakorn Oleksandr RANSAC
Robust Regression Methods for Computer Vision: A Review
*Peter J. Rousseeuw, Annick M. Leroy - Robust Regression and Outlier Detection
Daniela Viet
08.05 Robust Classification Nikolai Amir Learning with Noisy Labels
Label-noise robust logistic regression and applications
Thomas James
15.05 Robust Matrix Factorization Maida Aleksandar Robust PCA
Non-convex Robust PCA
Robust Nonnegative Matrix Factorization via L1 Norm Regularization
Robust Nonnegative Matrix Factorization Via Half-Quadratic Minimization
Robust Nonnegative Matrix Factorization
Boonyakorn Nikolai
22.05 Robust Clustering Csongor Roberto Noise Robust Spectral Clustering
Robust K-means: A Theoretical Revisit
Alexander Viet
29.05 Robust Community Detection Stevica Aleksandar On Community Outliers and their Efficient Detection in Information Networks
Focused Clustering and Outlier Detection in Large Attributed Graphs
Robust network community detection using balanced propagation
Nikolai Csongor
12.06 Robust Time Series / Sequence Modeling Lorenzo Oleksandr **Robust Statistics: Theory and Methods - Chapter 8
Learning an Outlier-Robust Kalman Filter
Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings
Thomas Daniela
19.06 Attacks on Classifiers Viet Aleksandar Poisoning Attacks against Support Vector Machines
Adversarial Label Flips Attack on Support Vector Machines
Evasion Attack of Multi-Class Linear Classifiers
Yuesong Stevica
26.06 Fooling Deep Networks James Aleksandar Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise
Yuesong Maida
03.07 Learning in the Adversarial Setting Daniela Aleksandar Adversarial Classification
Adversarial Support Vector Machine Learning
Robustness of classifiers: from adversarial to random noise
Boonyakorn Csongor
10.07 Learning from Crowds Yuesong Oleksandr Learning from Crowds
Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit
Alexander Lorenzo
17.07 Differential Privacy Thomas Roberto Differential Privacy
The Algorithmic Foundations of Differential Privacy
Signal Processing and Machine Learning with Differential Privacy
Maida James
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

** use Eaccess to access the PDFs of the chapters, i.e.

Organizational Details

  • 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


  • 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