Using Bayesian Logistic Regression with High-Order Interactions to Model Radiation-Induced Toxicities Following Radiotherapy. - Florian Buettner 001; Sarah Gulliford; Steve Webb; Mike Partridge (ICMLA, 2009)

Using a Bayesian Feature-selection Algorithm to Identify Dose-response Models Based on the Shape of the 3D Dose-distribution: An Example from a Head-and-neck Cancer Trial. - Florian Buettner 001; Sarah Gulliford; Steve Webb; Mike Partridge; Aisha B. Miah; Kevin J. Harrington; Christopher M. Nutting (ICMLA, 2010)

Document Informed Neural Autoregressive Topic Models. - Pankaj Gupta 003; Florian Buettner 001; Hinrich Schütze (CoRR, 2018)

textTOvec: Deep Contextualized Neural Autoregressive Models of Language with Distributed Compositional Prior. - Pankaj Gupta 003; Yatin Chaudhary; Florian Buettner 001; Hinrich Schütze (CoRR, 2018)

Document Informed Neural Autoregressive Topic Models with Distributional Prior. - Pankaj Gupta 003; Yatin Chaudhary; Florian Buettner 001; Hinrich Schütze (AAAI, 2019)

Texttovec: Deep Contextualized Neural autoregressive Topic Models of Language with Distributed Compositional Prior. - Pankaj Gupta 003; Yatin Chaudhary; Florian Buettner 001; Hinrich Schütze (ICLR (Poster), 2019)

TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification. - Yushan Liu 002; Markus M. Geipel; Christoph Tietz; Florian Buettner 001 (ECAI, 2020)

AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings. - Florian Buettner 001; John Piorkowski; Ian McCulloh; Ulli Waltinger (CoRR, 2020)

Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration. - Christian Tomani; Florian Buettner 001 (AAAI, 2021)

Post-Hoc Uncertainty Calibration for Domain Drift Scenarios. - Christian Tomani; Sebastian Gruber 001; Muhammed Ebrar Erdem; Daniel Cremers; Florian Buettner 001 (CVPR, 2021)

KDD Health Day/DSHealth 2021: Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare: State of XAI and Trustworthiness in Health. - Fei Wang 001; Prithwish Chakraborty; Tao Xu; Pei-Yun Sabrina Hsueh; Xudong Sun 014; Gregor Stiglic; Gracy Crane; Jiang Bian 001; Laleh Haghverdi; Lixia Yao; Florian Buettner 001 (KDD, 2021)

Multi-output Gaussian Processes for uncertainty-aware recommender systems. - Yinchong Yang; Florian Buettner 001 (UAI, 2021)

Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision. - Xudong Sun 014; Florian Buettner 001 (CoRR, 2021)

Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables. - Arber Qoku; Florian Buettner 001 (CoRR, 2021)

Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration. - Christian Tomani; Daniel Cremers; Florian Buettner 001 (ECCV (13), 2022)

Workshop on Applied Data Science for Healthcare (DSHealth): Transparent and Human-centered AI. - Tao Xu; Fei Wang 001; Prithwish Chakraborty; Pei-Yun Sabrina Hsueh; Gregor Stiglic; Jiang Bian 001; Lixia Yao; Alexej Gossmann; Florian Buettner 001 (KDD, 2022)

Better Uncertainty Calibration via Proper Scores for Classification and Beyond. - Sebastian G. Gruber; Florian Buettner 001 (NeurIPS, 2022)

Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting. - Alexander Koebler; Ralf Gross; Florian Buettner 001; Ingo Thon (ECML/PKDD (6), 2022)

Trustworthy Deep Learning via Proper Calibration Errors: A Unifying Approach for Quantifying the Reliability of Predictive Uncertainty. - Sebastian G. Gruber; Florian Buettner 001 (CoRR, 2022)

Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions. - Achim Hekler; Titus J. Brinker; Florian Buettner 001 (AAAI, 2023)

Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition. - Sebastian Gruber 001; Florian Buettner 001 (AISTATS, 2023)

Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity. - Arber Qoku; Florian Buettner 001 (AISTATS, 2023)

Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare. - Tao Xu; Fei Wang 001; Prithwish Chakraborty; Pei-Yun Sabrina Hsueh; Gregor Stiglic; Jiang Bian 001; Lixia Yao; Alexej Gossmann; Florian Buettner 001 (KDD, 2023)

Deep Learning Model for Video-Classification of Echocardiography Images. - Michela Destito; Paolo Zaffino; Jolanda Sabatino; Claudia Critelli; Arber Qoku; Florian Buettner 001; Salvatore De Rosa; Maria Francesca Spadea (MetroXRAINE, 2023)

Understanding metric-related pitfalls in image analysis validation. - Annika Reinke; Minu Tizabi; Michael Baumgartner 001; Matthias Eisenmann; Doreen Heckmann-Nötzel; A. Emre Kavur; Tim Rädsch; Carole H. Sudre; Laura Ación; Michela Antonelli; Tal Arbel; Spyridon Bakas; et al. (CoRR, 2023)

Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. - Teodora Popordanoska; Sebastian Gregor Gruber; Aleksei Tiulpin; Florian Büttner 001; Matthew B. Blaschko (AISTATS, 2024)

Through the Eyes of the Expert: Aligning Human and Machine Attention for Industrial AI. - Alexander Koebler; Christian Greisinger; Jan Paulus; Ingo Thon; Florian Buettner 001 (HCI (52), 2024)

MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model. - Alexander Koebler; Ingo Thon; Florian Buettner 001 (ICDM, 2024)

Provably Better Explanations with Optimized Aggregation of Feature Attributions. - Thomas Decker 004; Ananta R. Bhattarai; Jindong Gu; Volker Tresp; Florian Buettner 001 (ICML, 2024)

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models. - Sebastian Gregor Gruber; Florian Buettner 001 (ICML, 2024)

Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance. - Thomas Decker 004; Alexander Koebler; Michael Lebacher; Ingo Thon; Volker Tresp; Florian Buettner 001 (KDD, 2024)

DomainLab: A modular Python package for domain generalization in deep learning. - Xudong Sun 002; Carla Feistner; Alexej Gossmann; George Schwarz; Rao Muhammad Umer; Lisa Beer; Patrick Rockenschaub; Rahul Babu Shrestha; Armin Gruber; Nutan Chen; Sayedali Shetab Boushehri; Florian Buettner 001; et al. (CoRR, 2024)

Federated Continual Learning Goes Online: Leveraging Uncertainty for Modality-Agnostic Class-Incremental Learning. - Giuseppe Serra 004; Florian Buettner 001 (CoRR, 2024)

Disentangling Mean Embeddings for Better Diagnostics of Image Generators. - Sebastian G. Gruber; Pascal Tobias Ziegler; Florian Buettner 001 (CoRR, 2024)

Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention. - Alexander Koebler; Thomas Decker 004; Ingo Thon; Volker Tresp; Florian Buettner 001 (AISTATS, 2025)

Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond. - Giuseppe Serra 004; Florian Buettner 001 (ICLR, 2025)

Application-driven validation of posteriors in inverse problems. - Tim J. Adler; Jan-Hinrich Nölke; Annika Reinke; Minu Dietlinde Tizabi; Sebastian Gruber 001; Dasha Trofimova; Lynton Ardizzone; Paul F. Jaeger; Florian Buettner 001; Ullrich Köthe; Lena Maier-Hein (Medical Image Anal., 2025)

How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning. - Giuseppe Serra 004; Ben Werner; Florian Buettner 001 (Trans. Mach. Learn. Res., 2025)

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks. - Achim Hekler; Lukas Kuhn; Florian Buettner 001 (CoRR, 2025)

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations. - Thomas Decker 004; Volker Tresp; Florian Buettner 001 (CoRR, 2025)

An autonomous agent for auditing and improving the reliability of clinical AI models. - Lukas Kuhn; Florian Buettner 001 (CoRR, 2025)