Saved in:
| Main Authors: | Gruber, Cornelia, Schenk, Patrick Oliver, Schierholz, Malte, Kreuter, Frauke, Kauermann, Göran |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.16703 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Revisiting Active Learning under (Human) Label Variation
by: Gruber, Cornelia, et al.
Published: (2025)
by: Gruber, Cornelia, et al.
Published: (2025)
The Impact of Question Framing on the Performance of Automatic Occupation Coding
by: Kononykhina, Olga, et al.
Published: (2025)
by: Kononykhina, Olga, et al.
Published: (2025)
The Missing Link: Allocation Performance in Causal Machine Learning
by: Fischer-Abaigar, Unai, et al.
Published: (2024)
by: Fischer-Abaigar, Unai, et al.
Published: (2024)
Capturing Aleatoric Uncertainty in Climate Models
by: Gruber, Cornelia, et al.
Published: (2026)
by: Gruber, Cornelia, et al.
Published: (2026)
Position: There Is No Free Bayesian Uncertainty Quantification
by: Melev, Ivan, et al.
Published: (2025)
by: Melev, Ivan, et al.
Published: (2025)
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production
by: Schenk, Patrick Oliver, et al.
Published: (2024)
by: Schenk, Patrick Oliver, et al.
Published: (2024)
Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
by: Ball, Sarah, et al.
Published: (2024)
by: Ball, Sarah, et al.
Published: (2024)
How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness
by: Gordienko, Polina, et al.
Published: (2026)
by: Gordienko, Polina, et al.
Published: (2026)
Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty
by: Hechinger, Katharina, et al.
Published: (2023)
by: Hechinger, Katharina, et al.
Published: (2023)
Elements of Conformal Prediction for Statisticians
by: Sesia, Matteo, et al.
Published: (2026)
by: Sesia, Matteo, et al.
Published: (2026)
Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction
by: Meynent, Léo, et al.
Published: (2025)
by: Meynent, Léo, et al.
Published: (2025)
Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
by: Ball, Sarah, et al.
Published: (2025)
by: Ball, Sarah, et al.
Published: (2025)
From Ground Truth to Measurement: A Statistical Framework for Human Labeling
by: Chew, Robert, et al.
Published: (2026)
by: Chew, Robert, et al.
Published: (2026)
Bias in the Loop: How Humans Evaluate AI-Generated Suggestions
by: Beck, Jacob, et al.
Published: (2025)
by: Beck, Jacob, et al.
Published: (2025)
An Overview of Large Language Models for Statisticians
by: Ji, Wenlong, et al.
Published: (2025)
by: Ji, Wenlong, et al.
Published: (2025)
Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models
by: Hechinger, Katharina, et al.
Published: (2023)
by: Hechinger, Katharina, et al.
Published: (2023)
Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
by: Simson, Jan, et al.
Published: (2025)
by: Simson, Jan, et al.
Published: (2025)
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector
by: Fischer-Abaigar, Unai, et al.
Published: (2023)
by: Fischer-Abaigar, Unai, et al.
Published: (2023)
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
by: Kern, Christoph, et al.
Published: (2023)
by: Kern, Christoph, et al.
Published: (2023)
A Novel Framework for Uncertainty Quantification via Proper Scores for Classification and Beyond
by: Gruber, Sebastian G.
Published: (2025)
by: Gruber, Sebastian G.
Published: (2025)
Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
by: Gerlach, Thore, et al.
Published: (2025)
by: Gerlach, Thore, et al.
Published: (2025)
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
by: Gruber, Sebastian G., et al.
Published: (2022)
by: Gruber, Sebastian G., et al.
Published: (2022)
Deriving Duration Time from Occupancy Data -- A case study in the length of stay in Intensive Care Units for COVID-19 patients
by: Rave, Martje, et al.
Published: (2025)
by: Rave, Martje, et al.
Published: (2025)
Grappa -- A Machine Learned Molecular Mechanics Force Field
by: Seute, Leif, et al.
Published: (2024)
by: Seute, Leif, et al.
Published: (2024)
SatDINO: A Deep Dive into Self-Supervised Pretraining for Remote Sensing
by: Straka, Jakub, et al.
Published: (2025)
by: Straka, Jakub, et al.
Published: (2025)
Uncertainty in Machine Learning
by: Weytjens, Hans, et al.
Published: (2025)
by: Weytjens, Hans, et al.
Published: (2025)
The Skellam Distribution revisited -Estimating the unobserved incoming and outgoing ICU COVID-19 patients on a regional level in Germany
by: Rave, Martje, et al.
Published: (2023)
by: Rave, Martje, et al.
Published: (2023)
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
by: Sischka, Benjamin, et al.
Published: (2023)
by: Sischka, Benjamin, et al.
Published: (2023)
Using LASSO for Variable Selection in Exponential Random Graph models
by: Buttazzo, Sergio, et al.
Published: (2024)
by: Buttazzo, Sergio, et al.
Published: (2024)
Regression‐based network‐flow and inner‐matrix reconstruction
by: Michael Lebacher, et al.
Published: (2024)
by: Michael Lebacher, et al.
Published: (2024)
Detecting Security-Relevant Methods using Multi-label Machine Learning
by: Johnson, Oshando, et al.
Published: (2024)
by: Johnson, Oshando, et al.
Published: (2024)
Position: Embracing Negative Results in Machine Learning
by: Karl, Florian, et al.
Published: (2024)
by: Karl, Florian, et al.
Published: (2024)
Comparing Machine Learning Algorithms by Union-Free Generic Depth
by: Blocher, Hannah, et al.
Published: (2023)
by: Blocher, Hannah, et al.
Published: (2023)
A Unified Density Operator View of Flow Control and Merging
by: De Santi, Riccardo, et al.
Published: (2026)
by: De Santi, Riccardo, et al.
Published: (2026)
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
by: Ghugare, Raj, et al.
Published: (2024)
by: Ghugare, Raj, et al.
Published: (2024)
Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
by: Neubert, Marlen, et al.
Published: (2025)
by: Neubert, Marlen, et al.
Published: (2025)
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance
by: Peng, Siyuan, et al.
Published: (2024)
by: Peng, Siyuan, et al.
Published: (2024)
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
by: Blocher, Hannah, et al.
Published: (2023)
by: Blocher, Hannah, et al.
Published: (2023)
Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering
by: Ling, Huaming, et al.
Published: (2024)
by: Ling, Huaming, et al.
Published: (2024)
Learning Curves for Decision Making in Supervised Machine Learning: A Survey
by: Mohr, Felix, et al.
Published: (2022)
by: Mohr, Felix, et al.
Published: (2022)
Similar Items
-
Revisiting Active Learning under (Human) Label Variation
by: Gruber, Cornelia, et al.
Published: (2025) -
The Impact of Question Framing on the Performance of Automatic Occupation Coding
by: Kononykhina, Olga, et al.
Published: (2025) -
The Missing Link: Allocation Performance in Causal Machine Learning
by: Fischer-Abaigar, Unai, et al.
Published: (2024) -
Capturing Aleatoric Uncertainty in Climate Models
by: Gruber, Cornelia, et al.
Published: (2026) -
Position: There Is No Free Bayesian Uncertainty Quantification
by: Melev, Ivan, et al.
Published: (2025)