I am passionate about machine learning and related fields.

My interests are:

• Preference Learning
• Meta-Learning
• Intelligent Decision Making / Reinforcement Learning
• Computer Vision

The ML methods I am currently interested are:

• Probabilistic Ranking Models
• (Deep) Neural Networks
• Online Algorithms (for Large Scale Applications)
• Genetic Algorithms
• MDS techniques

Edge as well as cloud computing are the major platforms for realizing ML based applications. As a software engineer I am therefore also interested in those exciting technologies.

For the purpose of learning from and the prediction of preferences I worked on a new supervised learning setting called dyad ranking (see thesis).

The following problems can be tackled with it:

• Classification (binary, multi-class, multi-label)
• Similarity Learning
• Label Ranking

Related problem settings are:

• Learning to Rank for Information Retrieval
• Collaborative Filtering & Ranking
• Dyadic Prediction
• Zero-shot Learning

Recent publications:

• Preference-based Reinforcement Learning using Dyad Ranking [paper]
• Dyad ranking using Plackett–Luce models based on joint feature representations [paper]
• A Latent-Feature Plackett-Luce Model for Dyad Ranking Completion [paper][slides]
• Plackett-Luce Networks for Dyad Ranking [paper][slides][code]
• Preference-Based Meta-Learning using Dyad Ranking: Recommending Algorithms in Cold-Start Situations [paper]
• Dyad Ranking using a Bilinear Plackett-Luce Model [paper]
• Nameling Discovery Challenge - Collaborative Neighborhoods [paper]