PhD students and Postdocs
- Leena Chennuru Vankadara (PhD student)
- Luca Rendsburg (PhD student)
-
Michael Lohaus (PhD student)
- Diego Fioravanti (PhD student)
- Damien Garreau (Postdoc)
- Michael Perrot (Postdoc)
- Debarghya Ghoshdastidar (Postdoc)
- Siavash Haghiri (Phd student)
- Matthäus Kleindessner (PhD student)
- Lennard Schulz (PhD student)
- Tobias Lang (Postdoc)
- Morteza Alamgir (PhD student)
- Sven Kurras (PhD student)
- Samory Kpotufe (Postdoc)
- Agnes Radl (Postdoc)
- Markus Maier (PhD student)
Long term visitors
- Albert Agisha (Visitor)
- Oindrila Kanjilal (Postdoc)
- Cheng Tang (PhD student at George Washington University)
- Rita Morisi (PhD student, Institute of Advanced Studies in Lucca, Italy)
- Siavash Haghiri (Master student, Sharif University, Iran)
- Antoine Channarond (Postdoc at ENS Cachan, France)
- Yoshikazu Terada (PhD student at Osaka University, Japan)
- Dario Garcia (PhD student at Universidad Carlos III de Madrid, Spain)
- Samory Kpotufe (PhD student at the University of California at San Diego)
- Sebastien Bubeck (Master student at the Ecole normale superieure, France)
- Odalric-Ambrym Maillard (Master student at the Ecole normale superieure, France)
Undergraduate students (BSc and MSc thesis)
- Adam Koenig, BSc thesis CS: Approximating Shapley-Values
- Nico Sarink, BSc thesis: The Fused Unbalanced Gromov-Wasserstein Framework
- Amelie Schäfer (BSc thesis CS): Briefumschlag-Computer:
Simulation des Machine Learning Algorithmus und Aufarbeitung
für das Stadtmuseum Tübingen
- Johannes Hölscher (MSc thesis, CS):Perceptual reparameterization of image manipulation sliders
- Kornelius Raeth (MSc thesis ML): Evaluating Gibbs
Priors for Inductive Bias Discovery at the Example of
Reconstruction Methods
- Benedikt Gottschlich (MEd CS): Kinesthetic Learning Activities in Algorithm Lectures
- Frieder Göppert (MSc thesis, Cog.Sci.): Feature
Attribution Methods: Shapley Values on Logical Formulas and
Improving Explanations by Averaging
- Jonas König (MSc thesis CS): Hyperparameters improve Group Fairness for Binary Classification
- Julius Vetter (MSc thesis, CS): When can random graphs be described by low-rank matrices?
- Fynn Neurath (BSc thesis, Cog.Sci.): Validation of simulations for triplet experiments in psychophysics
- Rabanus Derr (MSc thesis, ML): Certain Fairness for Uncertain Regressors
- Tabea Frisch (BSc thesis in Cognitive Science): Data Deletion in Decision Trees
- Margareta Schlueter (BSc thesis in CS): Is ordinal embedding NP hard? pdf
- Solveig Klepper (MSc thesis in CS): Tangles in machine learning
- Rabanus Derr (BSc thesis in bioinformatics): Adversarial examples for k-nearest-neighbor classification
- Benjamin Hogl MSc thesis in CS): Fairness in machine learning with multiple protected groups
- Moritz Haas (MSc thesis in maths): Ranking with local comparisons
- Tobias Frangen (MSc thesis in maths): Consistency of relative neighborhood classification rules
- Leena Chennuru Vankadara (MSc thesis in CS): Metric Embeddings for Machine Learning
- Sascha Meyen (MSc thesis in CS): Relation between classification accuracy and mutual information
- Kai Frederking (BSc thesis in CS): The doubling dimension of geometric graphs
- Robert Kessler (MSc thesis in CS): Using ordinal comparisons in gaming
- Mehdi Sajjadi (MSc thesis in CS): Peer-grading algorithms: Mean estimator outperforms probabilistic models.
- Yuliia Orlova (MSc thesis in maths): On the Power of Graph Kernels
- Longshan Sun (MSc thesis in CS): Algorithms for peer grading
- Jonas Häring ( BSc thesis in maths): Comparing expander graphs to graphs with a low doubling dimension
- Alexis Engelke (BSc thesis in CS): Dynamic streaming algorithms for graph partitioning
- Sundus Israr (MSc thesis in maths): Graph kernels for brain networks
- Julian Busch (BSc thesis in CS): A randomized algorithm for balanced mincuts
- Rolf Köhler (MSc thesis in maths): Detecting the mincut in very sparse random graphs
- Philipp Drewe (MSc thesis in maths): Hierarchical clustering and density estimation based on kNN graphs
- Stephanie Jegelka (MSc thesis in CS): Statistical learning theory approaches to clustering
