Seminar: Robust Data Mining Techniques

News

  • 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.

Description

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).

 

Topics

DateTopicStudentSupervisorReferencesReviewer 1Reviewer 2
24.04Robust RegressionBoonyakornOleksandr

RANSAC

Robust Regression Methods for Computer Vision: A Review

*Peter J. Rousseeuw, Annick M. Leroy - Robust Regression and Outlier Detection

DanielaViet
08.05Robust ClassificationNikolaiAmir

Learning with Noisy Labels

Label-noise robust logistic regression and applications

ThomasJames
15.05Robust Matrix FactorizationMaidaAleksandar

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

BoonyakornNikolai
22.05Robust ClusteringCsongorRoberto

Noise Robust Spectral Clustering

Robust K-means: A Theoretical Revisit

AlexanderViet
29.05Robust Community DetectionStevicaAleksandar

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

NikolaiCsongor
12.06Robust Time Series / Sequence ModelingLorenzoOleksandr

**Robust Statistics: Theory and Methods - Chapter 8

Learning an Outlier-Robust Kalman Filter

Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings

ThomasDaniela
19.06Attacks on ClassifiersVietAleksandar

Poisoning Attacks against Support Vector Machines

Adversarial Label Flips Attack on Support Vector Machines

Evasion Attack of Multi-Class Linear Classifiers

YuesongStevica
26.06Fooling Deep NetworksJamesAleksandar

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

YuesongMaida
03.07Learning in the Adversarial SettingDanielaAleksandar

Adversarial Classification

Adversarial Support Vector Machine Learning

Robustness of classifiers: from adversarial to random noise

BoonyakornCsongor
10.07Learning from CrowdsYuesongOleksandr

Learning from Crowds

Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit

AlexanderLorenzo
17.07Differential PrivacyThomasRoberto

Differential Privacy

The Algorithmic Foundations of Differential Privacy

Signal Processing and Machine Learning with Differential Privacy

MaidaJames
24.07Robustness of Complex NetworksAlexanderOleksandrNetwork RobustnessStevicaLorenzo

 

* 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

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 kdd-seminar-robust@in.tum.de.

Prerequisites

  • 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.). 

Requirements

  • 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.

Dates

  • 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

Deadlines

  • 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