32 citations · 124 across the 18 of their papers we have counts for
18 papers
Using predictive multiplicity to measure individual performance within the AI Act
Karolin Frohnapfel, Mara Seyfert, Sebastian Bordt +2
When building AI systems for decision support, one often encounters the phenomenon of predictive multiplicity: a single best model does not exist; instead, one can construct many m…
Auditing Local Explanations is Hard
Robi Bhattacharjee, Ulrike von Luxburg
In sensitive contexts, providers of machine learning algorithms are increasingly required to give explanations for their algorithms' decisions. However, explanation receivers might…
Rethinking Explainable Machine Learning as Applied Statistics
Sebastian Bordt, Eric Raidl, Ulrike von Luxburg
In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used. In this position paper, w…
Pitfalls of Climate Network Construction: A Statistical Perspective
Moritz Haas, Bedartha Goswami, Ulrike von Luxburg
Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight t…
Relating graph auto-encoders to linear models
Solveig Klepper, Ulrike von Luxburg
Graph auto-encoders are widely used to construct graph representations in Euclidean vector spaces. However, it has already been pointed out empirically that linear models on many t…
From Shapley Values to Generalized Additive Models and back
Sebastian Bordt, Ulrike von Luxburg
In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconc…
Looking Deeper into Tabular LIME
Damien Garreau, Ulrike von Luxburg
In this paper, we present a thorough theoretical analysis of the default implementation of LIME in the case of tabular data. We prove that in the large sample limit, the interpreta…
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
Sebastian Bordt, Ulrike von Luxburg
Applications of machine learning inform human decision makers in a broad range of tasks. The resulting problem is usually formulated in terms of a single decision maker. We argue t…
Group Decisions based on Confidence Weighted Majority Voting
Sascha Meyen, Dorothee M. B. Sigg, Ulrike von Luxburg +1
Background: It has repeatedly been reported that when making decisions under uncertainty, groups outperform individuals. In a lab setting, real groups are often replaced by simulat…
Advancing Research on Unconscious Priming: When can Scientists Claim an Indirect Task Advantage?
Sascha Meyen, Iris A. Zerweck, Catarina Amado +2
Current literature holds that many cognitive functions can be performed outside consciousness. Evidence for this view comes from unconscious priming. In a typical experiment, visua…
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
Leena Chennuru Vankadara, Siavash Haghiri, Michael Lohaus +2
The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet comparisons of the form "Is item closer to th…
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier +1
The study of networks leads to a wide range of high dimensional inference problems. In many practical applications, one needs to draw inference from one or few large sparse network…
Comparison Based Nearest Neighbor Search
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. I…
Kernel functions based on triplet comparisons
Matthäus Kleindessner, Ulrike von Luxburg
Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel functio…
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis
Matthäus Kleindessner, Ulrike von Luxburg
In recent years it has become popular to study machine learning problems in a setting of ordinal distance information rather than numerical distance measurements. By ordinal distan…
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines
Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MO…
Construction of Tight Frames on Graphs and Application to Denoising
Franziska Göbel, Gilles Blanchard, Ulrike von Luxburg
Given a neighborhood graph representation of a finite set of points we construct a frame (redundant dictionary) for the space of real-valued func…
Unconscious lie detection as an example of a widespread fallacy in the Neurosciences
Volker H. Franz, Ulrike von Luxburg
Neuroscientists frequently use a certain statistical reasoning to establish the existence of distinct neuronal processes in the brain. We show that this reasoning is flawed and tha…