Kirchhof, Michael

11 publications

ICML 2025 Position: Uncertainty Quantification Needs Reassessment for Large Language Model Agents Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci

ICML 2025 Shielded Diffusion: Generating Novel and Diverse Images Using Sparse Repellency Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye, Marco Cuturi

NeurIPS 2024 Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh

ICMLW 2024 Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh

NeurIPSW 2024 Efficient and Effective Uncertainty Quantification for LLMs Miao Xiong, Andrea Santilli, Michael Kirchhof, Adam Golinski, Sinead Williamson

NeurIPSW 2024 On a Spurious Interaction Between Uncertainty Scores and Answer Evaluation Metrics in Generative QA Tasks Andrea Santilli, Miao Xiong, Michael Kirchhof, Pau Rodriguez, Federico Danieli, Xavier Suau, Luca Zappella, Sinead Williamson, Adam Golinski

ICML 2023 Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh

NeurIPS 2023 URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh, Dr. Enkelejda Kasneci

UAI 2023 When Are Post-Hoc Conceptual Explanations Identifiable? Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci

ECCV 2022 A Non-Isotropic Probabilistic Take on Proxy-Based Deep Metric Learning Michael Kirchhof, Karsten Roth, Zeynep Akata, Enkelejda Kasneci

UAI 2021 pRSL: Interpretable Multi-Label Stacking by Learning Probabilistic Rules Michael Kirchhof, Lena Schmid, Christopher Reining, Michael Hompel, Markus Pauly