Tobias Leemann

According to our database, Tobias Leemann authored at least 20 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep Neural Networks and Tabular Data: A Survey.

Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci

IEEE Trans. Neural Networks Learn. Syst., June, 2024

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations.

Yao Rong, Tobias Leemann, Thai-trang Nguyen, Lisa Fiedler, Peizhu Qian, Vaibhav V. Unhelkar, Tina Seidel, Gjergji Kasneci, Enkelejda Kasneci

IEEE Trans. Pattern Anal. Mach. Intell., April, 2024

Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation.

Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergül Aydöre

CoRR, 2024

Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers.

Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci

CoRR, 2024

Towards Non-adversarial Algorithmic Recourse.

Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj, Gjergji Kasneci

Proceedings of the Explainable Artificial Intelligence, 2024

Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals.

Bardh Prenkaj, Mario Villaizán-Vallelado, Tobias Leemann, Gjergji Kasneci

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI.

Miriam Schirmer, Tobias Leemann, Gjergji Kasneci, Jürgen Pfeffer, David Jurgens

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data.

Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes.

Bardh Prenkaj, Mario Villaizán-Vallelado, Tobias Leemann, Gjergji Kasneci

CoRR, 2023

When are post-hoc conceptual explanations identifiable?

Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Gaussian Membership Inference Privacy.

Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Trade-Off between Actionable Explanations and the Right to be Forgotten.

Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Language Models are Realistic Tabular Data Generators.

Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization.

Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci

CoRR, 2022

Towards Human-centered Explainable AI: User Studies for Model Explanations.

Yao Rong, Tobias Leemann, Thai-trang Nguyen, Lisa Fiedler, Tina Seidel, Gjergji Kasneci, Enkelejda Kasneci

CoRR, 2022

Disentangling Embedding Spaces with Minimal Distributional Assumptions.

Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci

CoRR, 2022

Evaluating Feature Attribution: An Information-Theoretic Perspective.

Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci

CoRR, 2022

A Consistent and Efficient Evaluation Strategy for Attribution Methods.

Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci

Proceedings of the International Conference on Machine Learning, 2022

2021

Multi-Step Training for Predicting Roundabout Traffic Situations.

Moritz Sackmann, Tobias Leemann, Henrik Bey, Ulrich Hofmann, Jörn Thielecke

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling.

Tobias Leemann, Moritz Sackmann, Jörn Thielecke, Ulrich Hofmann

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021