Research team
Expertise
My expertise: - Multivariate statistics - Robust statistics - Anomaly detection - Clustering - visualization - statistical machine learning
Statistical learning under cellwise contamination.
Abstract
This proposal bridges knowledge from robust statistics, machine learning and optimization and builds on my very recent work on robust covariance estimation to introduce general frameworks for unsupervised and supervised statistical learning under cellwise contamination. The project considers covariance estimation, principal component analysis, linear regression and logistic regression. It allows for extensions in the direction of regularized estimation and nonlinear modeling using kernels. In addition to the development of methodology, the project aims to assess the gravity of cellwise contamination in practical challenges by collaborating with experts on macro-economic time series modeling and drug development.Researcher(s)
- Promoter: Raymaekers Jakob
- Fellow: Raymaekers Jakob
Research team(s)
Project type(s)
- Research Project
Robust Directed Acyclic Graph Learning for Causal Modeling.
Abstract
Due to technological advances, the available amount of data has increased exponentially over the last decade. The field of data science (DS) has followed this growth as it provides an indispensable tool for translating data into insight and knowledge. Where DS was traditionally concerned with learning associations in data, it has become clear in recent times that causal relations often provide a deeper understanding of the data and a stronger tool in many practical applications. One of the established approaches to causal modeling is to use a directed acyclical graph (DAG) to represent the causal relations. These DAGs have to be learned based on observed data. Many of the SOTA techniques for DAG learning are very sensitive to anomalies, and yield unreliable results in their presence. We aim to develop methods for DAG learning that remain efficient and reliable under contamination of the data. The project starts by building a solid foundation for the concepts of robustness in DAG learning. Building upon these foundations, we will then proceed to build a general robust DAG learning methodology. The project envisions three different but complementary approaches to the development of robust DAG learning methods. The developed methodology will be evaluated theoretically and empirically, and tested in a variety of real world cases.Researcher(s)
- Promoter: Verdonck Tim
- Co-promoter: Latré Steven
- Co-promoter: Raymaekers Jakob
- Fellow: Leyder Sarah
Research team(s)
Project type(s)
- Research Project