Saved in:
| Main Authors: | Helgerth, Lena, Christmann, Andreas |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.05808 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Central Role of the Loss Function in Reinforcement Learning
by: Wang, Kaiwen, et al.
Published: (2024)
by: Wang, Kaiwen, et al.
Published: (2024)
Federated Optimization of Smooth Loss Functions
by: Jadbabaie, Ali, et al.
Published: (2022)
by: Jadbabaie, Ali, et al.
Published: (2022)
Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
by: Martin-Maroto, Fernando, et al.
Published: (2026)
by: Martin-Maroto, Fernando, et al.
Published: (2026)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty
by: Charpentier, Arthur, et al.
Published: (2026)
by: Charpentier, Arthur, et al.
Published: (2026)
Refined Risk Bounds for Unbounded Losses via Transductive Priors
by: Qian, Jian, et al.
Published: (2024)
by: Qian, Jian, et al.
Published: (2024)
Riesz Regression As Direct Density Ratio Estimation
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
How to Measure Evidence and Its Strength: Bayes Factors or Relative Belief Ratios?
by: Al-Labadi, Luai, et al.
Published: (2023)
by: Al-Labadi, Luai, et al.
Published: (2023)
A Central Limit Theorem for the permutation importance measure
by: Föge, Nico, et al.
Published: (2024)
by: Föge, Nico, et al.
Published: (2024)
Conformal Risk Control for Non-Monotonic Losses
by: Angelopoulos, Anastasios N.
Published: (2026)
by: Angelopoulos, Anastasios N.
Published: (2026)
Adaptive Refinement Protocols for Distributed Distribution Estimation under $\ell^p$-Losses
by: Yuan, Deheng, et al.
Published: (2024)
by: Yuan, Deheng, et al.
Published: (2024)
Functional Linear Regression of Cumulative Distribution Functions
by: Zhang, Qian, et al.
Published: (2022)
by: Zhang, Qian, et al.
Published: (2022)
Learning from Samples: Inverse Problems over measures via Sharpened Fenchel-Young Losses
by: Andrade, Francisco, et al.
Published: (2025)
by: Andrade, Francisco, et al.
Published: (2025)
Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
by: Garg, Anvit, et al.
Published: (2025)
by: Garg, Anvit, et al.
Published: (2025)
Ratio Covers of Convex Sets and Optimal Mixture Density Estimation
by: Compton, Spencer, et al.
Published: (2026)
by: Compton, Spencer, et al.
Published: (2026)
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
by: Michel, Thomas, et al.
Published: (2025)
by: Michel, Thomas, et al.
Published: (2025)
Transfer Learning Beyond Bounded Density Ratios
by: Kalavasis, Alkis, et al.
Published: (2024)
by: Kalavasis, Alkis, et al.
Published: (2024)
Statistical Decision Theory with Counterfactual Loss
by: Koch, Benedikt, et al.
Published: (2025)
by: Koch, Benedikt, et al.
Published: (2025)
On Uncertainty Calibration for Equivariant Functions
by: Berman, Edward, et al.
Published: (2025)
by: Berman, Edward, et al.
Published: (2025)
Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024)
by: Zhou, Tian-Yi, et al.
Published: (2024)
Centrality Estimators for Probability Density Functions
by: Ziou, Djemel
Published: (2024)
by: Ziou, Djemel
Published: (2024)
Thermodynamic Response Functions in Singular Bayesian Models
by: Plummer, Sean
Published: (2026)
by: Plummer, Sean
Published: (2026)
Functional Bias and Tangent-Space Geometry in Variational Inference
by: Plummer, Sean
Published: (2026)
by: Plummer, Sean
Published: (2026)
Tensor Product Neural Networks for Functional ANOVA Model
by: Park, Seokhun, et al.
Published: (2025)
by: Park, Seokhun, et al.
Published: (2025)
Statistical Learning Guarantees for Group-Invariant Barron Functions
by: Yang, Yahong, et al.
Published: (2025)
by: Yang, Yahong, et al.
Published: (2025)
When Are Trade-Off Functions Testable from Finite Samples?
by: Shi, Kaining, et al.
Published: (2026)
by: Shi, Kaining, et al.
Published: (2026)
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
by: Kremer, Heiner, et al.
Published: (2022)
by: Kremer, Heiner, et al.
Published: (2022)
Exploring the Complexity of Deep Neural Networks through Functional Equivalence
by: Shen, Guohao
Published: (2023)
by: Shen, Guohao
Published: (2023)
Bounds in Wasserstein Distance for Locally Stationary Functional Time Series
by: Tinio, Jan Nino G., et al.
Published: (2025)
by: Tinio, Jan Nino G., et al.
Published: (2025)
Training Implicit Generative Models via an Invariant Statistical Loss
by: de Frutos, José Manuel, et al.
Published: (2024)
by: de Frutos, José Manuel, et al.
Published: (2024)
A Theory of Nonparametric Covariance Function Estimation for Discretely Observed Data
by: Terada, Yoshikazu, et al.
Published: (2026)
by: Terada, Yoshikazu, et al.
Published: (2026)
Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks
by: Zhao, Yuzhen, et al.
Published: (2026)
by: Zhao, Yuzhen, et al.
Published: (2026)
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional
by: McGrath, Sean, et al.
Published: (2022)
by: McGrath, Sean, et al.
Published: (2022)
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
by: Rabel, Martin, et al.
Published: (2024)
by: Rabel, Martin, et al.
Published: (2024)
Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
by: McGrath, Sean, et al.
Published: (2025)
by: McGrath, Sean, et al.
Published: (2025)
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses
by: Abdelaleem, Eslam, et al.
Published: (2023)
by: Abdelaleem, Eslam, et al.
Published: (2023)
Relationship between Hölder Divergence and Functional Density Power Divergence: Intersection and Generalization
by: Kobayashi, Masahiro
Published: (2025)
by: Kobayashi, Masahiro
Published: (2025)
Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions
by: Kanj, Haitham, et al.
Published: (2026)
by: Kanj, Haitham, et al.
Published: (2026)
Functional Sequential Treatment Allocation with Covariates
by: Kock, Anders Bredahl, et al.
Published: (2020)
by: Kock, Anders Bredahl, et al.
Published: (2020)
Similar Items
-
The Central Role of the Loss Function in Reinforcement Learning
by: Wang, Kaiwen, et al.
Published: (2024) -
Federated Optimization of Smooth Loss Functions
by: Jadbabaie, Ali, et al.
Published: (2022) -
Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
by: Martin-Maroto, Fernando, et al.
Published: (2026) -
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024) -
Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty
by: Charpentier, Arthur, et al.
Published: (2026)