Cover Image

Density-Ratio Losses for Post-Hoc Learning to Defer

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Soen, Alexander, Thobaben, Ragnar, Jaldén, Joakim, Nock, Richard
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rejection via Learning Density Ratios
by: Soen, Alexander, et al.
Published: (2024)

A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
by: Filatrella, Dario, et al.
Published: (2026)

An Information-Theoretic Approach to Generalization Theory
by: Rodríguez-Gálvez, Borja, et al.
Published: (2024)

More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
by: Rodríguez-Gálvez, Borja, et al.
Published: (2023)

Tempered Calculus for ML: Application to Hyperbolic Model Embedding
by: Nock, Richard, et al.
Published: (2024)

A note on generalization bounds for losses with finite moments
by: Rodríguez-Gálvez, Borja, et al.
Published: (2024)

A Connection Between Learning to Reject and Bhattacharyya Divergences
by: Soen, Alexander
Published: (2025)

How to Boost Any Loss Function
by: Nock, Richard, et al.
Published: (2024)

A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry
by: Lindström, Martin, et al.
Published: (2024)

Trade-Offs of Diagonal Fisher Information Matrix Estimators
by: Soen, Alexander, et al.
Published: (2024)

Binary Losses for Density Ratio Estimation
by: Zellinger, Werner
Published: (2024)

Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
by: Mao, Anqi, et al.
Published: (2024)

Boosting gets full Attention for Relational Learning
by: Guillame-Bert, Mathieu, et al.
Published: (2024)

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
by: Pugnana, Andrea, et al.
Published: (2025)

pyBregMan: A Python library for Bregman Manifolds
by: Nielsen, Frank, et al.
Published: (2024)

Post-Hoc Calibrated Anomaly Detection
by: Gloumeau, Sean
Published: (2025)

Density Ratio-based Proxy Causal Learning Without Density Ratios
by: Bozkurt, Bariscan, et al.
Published: (2025)

A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
by: Charusaie, Mohammad-Amin, et al.
Published: (2024)

Learning to Partially Defer for Sequences
by: Rayan, Sahana, et al.
Published: (2025)

Understanding Disparities in Post Hoc Machine Learning Explanation
by: Mhasawade, Vishwali, et al.
Published: (2024)

Learning-to-Defer with Expert-Conditional Advice
by: Montreuil, Yannis, et al.
Published: (2026)

Online Learning-to-Defer with Varying Experts
by: Duy, Dang Hoang, et al.
Published: (2026)

Generative Forests
by: Nock, Richard, et al.
Published: (2023)

A series of maximum entropy upper bounds of the differential entropy
by: Nielsen, Frank, et al.
Published: (2016)

Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model
by: Barker, Charmaine, et al.
Published: (2025)

Adversarial Robustness in One-Stage Learning-to-Defer
by: Montreuil, Yannis, et al.
Published: (2025)

Principled Approaches for Learning to Defer with Multiple Experts
by: Mao, Anqi, et al.
Published: (2023)

Domain Adaptation and Entanglement: an Optimal Transport Perspective
by: Koç, Okan, et al.
Published: (2025)

Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data
by: Calderon, Pio, et al.
Published: (2021)

Learning to Defer: A Survey
by: Strong, Joshua, et al.
Published: (2025)

Learning to Defer to a Population: A Meta-Learning Approach
by: Tailor, Dharmesh, et al.
Published: (2024)

Fatigue-Aware Learning to Defer via Constrained Optimisation
by: Zhang, Zheng, et al.
Published: (2026)

Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy
by: Quint, Eleanor
Published: (2026)

Harnessing Density Ratios for Online Reinforcement Learning
by: Amortila, Philip, et al.
Published: (2024)

Learning to Defer for Causal Discovery with Imperfect Experts
by: Clivio, Oscar, et al.
Published: (2025)

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
by: Montreuil, Yannis, et al.
Published: (2026)

Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
by: Montreuil, Yannis, et al.
Published: (2025)

Modelling the nanopore sequencing process with Helicase HMMs
by: Xu, Xuechun, et al.
Published: (2024)

Post Hoc Regression Refinement via Pairwise Rankings
by: Wijaya, Kevin Tirta, et al.
Published: (2025)

Post-Hoc Reversal: Are We Selecting Models Prematurely?
by: Ranjan, Rishabh, et al.
Published: (2024)