Shared source CLI essentials - exploring Microsoft's Rotor and the ECMA CLI. - David Stutz; Ted Neward; Geoff Shilling (O'Reilly, 2003)

Superpixel Segmentation: An Evaluation. - David Stutz (GCPR, 2015)

Learning 3D Shape Completion From Laser Scan Data With Weak Supervision. - David Stutz; Andreas Geiger 001 (CVPR, 2018)

Superpixels: An evaluation of the state-of-the-art. - David Stutz; Alexander Hermans; Bastian Leibe (Comput. Vis. Image Underst., 2018)

Disentangling Adversarial Robustness and Generalization. - David Stutz; Matthias Hein 001; Bernt Schiele (CVPR, 2019)

Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training. - David Stutz; Matthias Hein 001; Bernt Schiele (CoRR, 2019)

Adversarial Training Against Location-Optimized Adversarial Patches. - Sukrut Rao; David Stutz; Bernt Schiele (ECCV Workshops (5), 2020)

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. - David Stutz; Matthias Hein 001; Bernt Schiele (ICML, 2020)

On Mitigating Random and Adversarial Bit Errors. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (CoRR, 2020)

Learning 3D Shape Completion Under Weak Supervision. - David Stutz; Andreas Geiger 001 (Int. J. Comput. Vis., 2020)

Relating Adversarially Robust Generalization to Flat Minima. - David Stutz; Matthias Hein 001; Bernt Schiele (ICCV, 2021)

Bit Error Robustness for Energy-Efficient DNN Accelerators. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (MLSys, 2021)

A Closer Look at the Adversarial Robustness of Information Bottleneck Models. - Iryna Korshunova; David Stutz; Alexander A. Alemi; Olivia Wiles; Sven Gowal (CoRR, 2021)

Improving Robustness by Enhancing Weak Subnets. - Yong Guo; David Stutz; Bernt Schiele (ECCV (24), 2022)

Learning Optimal Conformal Classifiers. - David Stutz; Krishnamurthy Dvijotham; Ali Taylan Cemgil; Arnaud Doucet (ICLR, 2022)

Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets. - Yong Guo; David Stutz; Bernt Schiele (CoRR, 2022)

On Fragile Features and Batch Normalization in Adversarial Training. - Nils Philipp Walter; David Stutz; Bernt Schiele (CoRR, 2022)

Understanding and improving robustness and uncertainty estimation in deep learning. - David Stutz (2022)

Improving Robustness of Vision Transformers by Reducing Sensitivity to Patch Corruptions. - Yong Guo; David Stutz; Bernt Schiele (CVPR, 2023)

Certified Robust Models with Slack Control and Large Lipschitz Constants. - Max Maria Losch; David Stutz; Bernt Schiele; Mario Fritz (DAGM, 2023)

Robustifying Token Attention for Vision Transformers. - Yong Guo; David Stutz; Bernt Schiele (ICCV, 2023)

Evaluating AI systems under uncertain ground truth: a case study in dermatology. - David Stutz; Ali Taylan Cemgil; Abhijit Guha Roy; Tatiana Matejovicova; Melih Barsbey; Patricia Strachan; Mike Schaekermann; Jan Freyberg; Rajeev V. Rikhye; Beverly Freeman; Javier Perez Matos; Umesh Telang; et al. (CoRR, 2023)

Unlocking Accuracy and Fairness in Differentially Private Image Classification. - Leonard Berrada; Soham De; Judy Hanwen Shen; Jamie Hayes; Robert Stanforth; David Stutz; Pushmeet Kohli; Samuel L. Smith; Borja Balle (CoRR, 2023)

Conformal prediction under ambiguous ground truth. - David Stutz; Abhijit Guha Roy; Tatiana Matejovicova; Patricia Strachan; Ali Taylan Cemgil; Arnaud Doucet (Trans. Mach. Learn. Res., 2023)

Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

On Adversarial Training without Perturbing all Examples. - Max Maria Losch; Mohamed Omran; David Stutz; Mario Fritz; Bernt Schiele (ICLR, 2024)

Conformalized Credal Set Predictors. - Alireza Javanmardi; David Stutz; Eyke Hüllermeier (NeurIPS, 2024)

Capabilities of Gemini Models in Medicine. - Khaled Saab 003; Tao Tu 001; Wei-Hung Weng; Ryutaro Tanno; David Stutz; Ellery Wulczyn; Fan Zhang; Tim Strother; Chunjong Park; Elahe Vedadi; Juanma Zambrano Chaves; Szu-Yeu Hu; et al. (CoRR, 2024)

Mitigating LLM Hallucinations via Conformal Abstention. - Yasin Abbasi-Yadkori; Ilja Kuzborskij; David Stutz; András György 001; Adam Fisch; Arnaud Doucet; Iuliya Beloshapka; Wei-Hung Weng; Yao-Yuan Yang; Csaba Szepesvári; Ali Taylan Cemgil; Nenad Tomasev (CoRR, 2024)

Evaluating medical AI systems in dermatology under uncertain ground truth. - David Stutz; Ali Taylan Cemgil; Abhijit Guha Roy; Tatiana Matejovicova; Melih Barsbey; Patricia Strachan; Mike Schaekermann; Jan Freyberg; Rajeev Rikhye; Beverly Freeman; Javier Perez Matos; Umesh Telang; et al. (Medical Image Anal., 2025)

Conformalized Credal Regions for Classification with Ambiguous Ground Truth. - Michele Caprio; David Stutz; Shuo Li; Arnaud Doucet (Trans. Mach. Learn. Res., 2025)

Towards Conversational AI for Disease Management. - Anil Palepu; Valentin Liévin; Wei-Hung Weng; Khaled Saab 003; David Stutz; Yong Cheng; Kavita Kulkarni; S. Sara Mahdavi; Joelle K. Barral; Dale R. Webster; Katherine Chou; Avinatan Hassidim; et al. (CoRR, 2025)

Advancing Conversational Diagnostic AI with Multimodal Reasoning. - Khaled Saab 003; Jan Freyberg; Chunjong Park; Tim Strother; Yong Cheng; Wei-Hung Weng; David G. T. Barrett; David Stutz; Nenad Tomasev; Anil Palepu; Valentin Liévin; Yash Sharma; et al. (CoRR, 2025)

Towards physician-centered oversight of conversational diagnostic AI. - Elahe Vedadi; David G. T. Barrett; Natalie Harris; Ellery Wulczyn; Shashir Reddy; Roma Ruparel; Mike Schaekermann; Tim Strother; Ryutaro Tanno; Yash Sharma; Jihyeon Lee; Cían Hughes; et al. (CoRR, 2025)