Anna Hedström

According to our database, Anna Hedström authored at least 17 papers between 2022 and 2026.

Collaborative distances:

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

2026

Benchmarking XAI Explanations with Human-Aligned Evaluations.

Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier, Anna Hedström, Patricia Delhomme, David Filliat, Nicolas Bousquet, Goran Frehse, Massimiliano Mancini, Baptiste Caramiaux, Andrea Passerini, Gianni Franchi

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework.

Laura Kopf, Nils Feldhus, Kirill Bykov, Philine Lou Bommer, Anna Hedström, Marina M.-C. Höhne, Oliver Eberle

CoRR, June, 2025

Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions.

Anna Hedström, Philine Lou Bommer, Thomas F. Burns, Sebastian Lapuschkin, Wojciech Samek, Marina M.-C. Höhne

Trans. Mach. Learn. Res., 2025

To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models.

Anna Hedström, Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Manuela Veloso

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution.

Carlos Eiras-Franco, Anna Hedström, Marina M.-C. Höhne

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Benchmarking XAI Explanations with Human-Aligned Evaluations.

Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier, Anna Hedström, Patricia Delhomme, Nicolas Bousquet, Goran Frehse, Massimiliano Mancini, Baptiste Caramiaux, Andrea Passerini, Gianni Franchi

CoRR, 2024

Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond.

Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström, Niklas Schmolenski, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2024

Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test.

Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne

CoRR, 2024

A Fresh Look at Sanity Checks for Saliency Maps.

Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne

Proceedings of the Explainable Artificial Intelligence, 2024

CoSy: Evaluating Textual Explanations of Neurons.

Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability.

Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne

Proceedings of the 44. GIL-Jahrestagung, Informatik in der Land-, Forst- und Ernährungswirtschaft, 2024

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation.

Kristoffer Wickstrøm, Marina M.-C. Höhne, Anna Hedström

Proceedings of the Computer Vision - ECCV 2024 Workshops, 2024

2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.

Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

Trans. Mach. Learn. Res., 2023

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond.

Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

J. Mach. Learn. Res., 2023

Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science.

Philine Lou Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M.-C. Höhne

CoRR, 2023

2022

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations.

Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

CoRR, 2022

NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights.

Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022