Grégoire Montavon

Berliner Institut für Sozialforschung — Germany

Computer Science · Physical Sciences

32h-index19.0kcitations131works3.62yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Explainable Artificial Intelligence (XAI)(51), Machine Learning and Data Classification(24), Anomaly Detection Techniques and Applications(19), Adversarial Robustness in Machine Learning(18), Advanced Neural Network Applications(11)

Publications132 total

Investigating the Robustness of Subtask Distillation under Spurious Correlation
ArXiv.org·2026Open Access
Investigating the Robustness of Subtask Distillation under Spurious Correlation
arXiv (Cornell University)·2026Open Access
Distilling Lightweight Domain Experts from Large ML Models by Identifying Relevant Subspaces
arXiv (Cornell University)·2026Open Access
Distilling Lightweight Domain Experts from Large ML Models by Identifying Relevant Subspaces
ArXiv.org·2026Open Access
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena
IEEE Transactions on Pattern Analysis and Machine Intelligence·2026Open Access
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena
IEEE Transactions on Pattern Analysis and Machine Intelligence·2026Open Access
Towards desiderata-driven design of visual counterfactual explainers
Pattern Recognition·2025Open Access
Drainage: A Unifying Framework for Addressing Class Uncertainty
arXiv (Cornell University)·2025Open Access
Towards Symbolic XAI – Explanation Through Human Understandable Logical Relationships Between Features
2025
Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples
arXiv (Cornell University)·2025Open Access
XpertAI: Uncovering Regression Model Strategies for Sub-manifolds
Communications in computer and information science·2025· 1 citedOpen Access
Uncovering the Structure of Explanation Quality with Spectral Analysis
Communications in computer and information science·2025Open Access
MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification
Lecture notes in computer science·2025Open Access
Large Language Models Improve Cancer Survival Prediction Using Real-World Clinical Notes
2025Open Access
Neural interaction explainable AI predicts drug response across cancers
NAR Cancer·2025· 2 citedOpen Access
Towards Desiderata-Driven Design of Visual Counterfactual Explainers
ArXiv.org·2025Open Access
Explainable AI reveals Clever Hans effects in unsupervised learning models
Nature Machine Intelligence·2025· 15 citedOpen Access
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
Nature Cancer·2025· 32 citedOpen Access
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Journal of Chemical Theory and Computation·2025· 10 citedOpen Access
Towards symbolic XAI — explanation through human understandable logical relationships between features
Information Fusion·2025· 6 citedOpen Access
page 1 of 7Next →

Frequent Co-authors

Klaus‐Robert Müller(42), Wojciech Samek(27), Alexander Binder(13), Sebastian Lapuschkin(13), Frederick Klauschen(6), Jacob Kauffmann(5), Sebastian Bach(4), Oliver Eberle(4), Michael Bockmayr(4), Lukas Ruff(4), Philipp Keyl(4), Christopher J. Anders(3), Katja Hansen(3), Matthias Rupp(3), Alexandre Tkatchenko(3), Thomas Schnake(3), Jonas Lederer(3), Kristof T. Schütt(3), Klaus-Robert Müller(3), Philipp Jurmeister(3)