Buettner, Florian

24 publications

ICCV 2025 Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers Lukas Kuhn, Sari Sadiya, Jörg Schlötterer, Florian Buettner, Christin Seifert, Gemma Roig

ICLR 2025 Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond Giuseppe Serra, Florian Buettner

TMLR 2025 How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning Giuseppe Serra, Ben Werner, Florian Buettner

NeurIPS 2025 Improving Perturbation-Based Explanations by Understanding the Role of Uncertainty Calibration Thomas Decker, Volker Tresp, Florian Buettner

AISTATS 2025 Incremental Uncertainty-Aware Performance Monitoring with Active Labeling Intervention Alexander Koebler, Thomas Decker, Ingo Thon, Volker Tresp, Florian Buettner

ICLRW 2025 Towards LVLM-Aided Alignment of Task-Specific Vision Models Alexander Koebler, Christian Greisinger, Jan Paulus, Ingo Thon, Florian Buettner

ICLRW 2025 Why Uncertainty Calibration Matters for Reliable Perturbation-Based Explanations Thomas Decker, Volker Tresp, Florian Buettner

ICML 2024 A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models Sebastian Gregor Gruber, Florian Buettner

AISTATS 2024 Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Buettner, Matthew B. Blaschko

NeurIPSW 2024 Disentangling Mean Embeddings for Better Diagnostics of Image Generators Sebastian Gregor Gruber, Pascal Tobias Ziegler, Florian Buettner

NeurIPSW 2024 Incremental Uncertainty-Aware Performance Monitoring with Labeling Intervention Alexander Koebler, Thomas Decker, Ingo Thon, Volker Tresp, Florian Buettner

ICML 2024 Provably Better Explanations with Optimized Aggregation of Feature Attributions Thomas Decker, Ananta R. Bhattarai, Jindong Gu, Volker Tresp, Florian Buettner

AISTATS 2023 Encoding Domain Knowledge in Multi-View Latent Variable Models: A Bayesian Approach with Structured Sparsity Arber Qoku, Florian Buettner

AAAI 2023 Test Time Augmentation Meets Post-Hoc Calibration: Uncertainty Quantification Under Real-World Conditions Achim Hekler, Titus J. Brinker, Florian Buettner

NeurIPSW 2023 Towards Explanatory Model Monitoring Alexander Koebler, Thomas Decker, Michael Lebacher, Ingo Thon, Volker Tresp, Florian Buettner

AISTATS 2023 Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition Sebastian Gruber, Florian Buettner

NeurIPS 2022 Better Uncertainty Calibration via Proper Scores for Classification and Beyond Sebastian Gruber, Florian Buettner

ECML-PKDD 2022 Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting Alexander Koebler, Ralf Gross, Florian Buettner, Ingo Thon

ECCV 2022 Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration Christian Tomani, Daniel Cremers, Florian Buettner

UAI 2021 Multi-Output Gaussian Processes for Uncertainty-Aware Recommender Systems Yinchong Yang, Florian Buettner

CVPR 2021 Post-Hoc Uncertainty Calibration for Domain Drift Scenarios Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem, Daniel Cremers, Florian Buettner

AAAI 2021 Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration Christian Tomani, Florian Buettner

AAAI 2019 Document Informed Neural Autoregressive Topic Models with Distributional Prior Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze

ICLR 2019 textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS of LANGUAGE with DISTRIBUTED COMPOSITIONAL PRIOR Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schuetze