23 citations · 42 across the 13 of their papers we have counts for
13 papers
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci
Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty qu…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
Michael Kirchhof, James Thornton, Louis Béthune +3
The adoption of text-to-image diffusion models raises concerns over reliability, drawing scrutiny under the lens of various metrics like calibration, fairness, or compute efficienc…
Uncertainties of Latent Representations in Computer Vision
Michael Kirchhof
Uncertainty quantification is a key pillar of trustworthy machine learning. It enables safe reactions under unsafe inputs, like predicting only when the machine learning model dete…
Pretrained Visual Uncertainties
Michael Kirchhof, Mark Collier, Seong Joon Oh +1
Accurate uncertainty estimation is vital to trustworthy machine learning, yet uncertainties typically have to be learned for each task anew. This work introduces the first pretrain…
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty qua…
Trustworthy Machine Learning
Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen +2
As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distributi…
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh +1
Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising…
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh
Contrastively trained encoders have recently been proven to invert the data-generating process: they encode each input, e.g., an image, into the true latent vector that generated t…
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
Michael Kirchhof, Karsten Roth, Zeynep Akata +1
Proxy-based Deep Metric Learning (DML) learns deep representations by embedding images close to their class representatives (proxies), commonly with respect to the angle between th…
When are Post-hoc Conceptual Explanations Identifiable?
Tobias Leemann, Michael Kirchhof, Yao Rong +2
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discover…
pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
Michael Kirchhof, Lena Schmid, Christopher Reining +2
A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables applicatio…
Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network
Michael Kirchhof, Klaus Haas, Thomas Kornas +3
The production of lithium-ion battery cells is characterized by a high degree of complexity due to numerous cause-effect relationships between process characteristics. Knowledge ab…