1k citations · 1.3k across the 13 of their papers we have counts for
13 papers
The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI
Miriam Schirmer, Tobias Leemann, Gjergji Kasneci +2
Psychological trauma can manifest following various distressing events and is captured in diverse online contexts. However, studies traditionally focus on a single aspect of trauma…
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
Tobias Leemann, Alina Fastowski, Felix Pfeiffer +1
We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and b…
Towards Non-Adversarial Algorithmic Recourse
Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj +1
The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate th…
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
Bardh Prenkaj, Mario Villaizan-Vallelado, Tobias Leemann +1
We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages initial knowledge about the da…
Gaussian Membership Inference Privacy
Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
We propose a novel and practical privacy notion called -Membership Inference Privacy (-MIP), which explicitly considers the capabilities of realistic adversaries under the me…
I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data
Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle +1
We examine machine learning models in a setup where individuals have the choice to share optional personal information with a decision-making system, as seen in modern insurance pr…
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
Yao Rong, Tobias Leemann, Thai-trang Nguyen +6
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of e…
Language Models are Realistic Tabular Data Generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann +2
Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data's characteristics remains a significant chal…
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
Martin Pawelczyk, Tobias Leemann, Asia Biega +1
As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One…
When are Post-hoc Conceptual Explanations Identifiable?
Tobias Leemann, Michael Kirchhof, Yao Rong +2
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discover…
A Consistent and Efficient Evaluation Strategy for Attribution Methods
Yao Rong, Tobias Leemann, Vadim Borisov +2
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies. To assess the attribution quality across…
Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling
Tobias Leemann, Moritz Sackmann, Jörn Thielecke +1
Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast's uncertainty. The Multiple Hypotheses Predicti…
Deep Neural Networks and Tabular Data: A Survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler +3
Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, dee…