Image de couverture de livre

One-Stage Top-$k$ Learning-to-Defer: Score-Based Surrogates with Theoretical Guarantees

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Montreuil, Yannis, Carlier, Axel, Ng, Lai Xing, Ooi, Wei Tsang
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts
par: Montreuil, Yannis, et autres
Publié: (2025)

Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
par: Montreuil, Yannis, et autres
Publié: (2025)

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
par: Montreuil, Yannis, et autres
Publié: (2026)

Adversarial Robustness in One-Stage Learning-to-Defer
par: Montreuil, Yannis, et autres
Publié: (2025)

Learning-to-Defer with Expert-Conditional Advice
par: Montreuil, Yannis, et autres
Publié: (2026)

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
par: Montreuil, Yannis, et autres
Publié: (2024)

A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
par: Montreuil, Yannis, et autres
Publié: (2024)

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
par: Montreuil, Yannis, et autres
Publié: (2026)

Online Learning-to-Defer with Varying Experts
par: Duy, Dang Hoang, et autres
Publié: (2026)

Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer
par: Mao, Anqi, et autres
Publié: (2025)

Differentiable Knapsack and Top-k Operators via Dynamic Programming
par: Vivier-Ardisson, Germain, et autres
Publié: (2026)

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
par: Pugnana, Andrea, et autres
Publié: (2025)

Personalized Top-k Set Queries Over Predicted Scores
par: Nia, Sohrab Namazi, et autres
Publié: (2025)

Towards Robust Learning to Optimize with Theoretical Guarantees
par: Song, Qingyu, et autres
Publié: (2025)

FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
par: Fan, Flint Xiaofeng, et autres
Publié: (2024)

Theoretically Guaranteed Distribution Adaptable Learning
par: Xu, Chao, et autres
Publié: (2024)

Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees
par: Banerjee, Bisakh, et autres
Publié: (2025)

Learning to Partially Defer for Sequences
par: Rayan, Sahana, et autres
Publié: (2025)

$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval
par: Wang, Zihao, et autres
Publié: (2026)

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
par: Jin, Richeng, et autres
Publié: (2020)

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
par: Montasser, Omar, et autres
Publié: (2024)

Principled Approaches for Learning to Defer with Multiple Experts
par: Mao, Anqi, et autres
Publié: (2023)

Enhanced Parcel Arrival Forecasting for Logistic Hubs: An Ensemble Deep Learning Approach
par: Pan, Xinyue, et autres
Publié: (2026)

One-Step Score-Based Density Ratio Estimation
par: Chen, Wei, et autres
Publié: (2026)

Top-$k$ Feature Importance Ranking
par: Chen, Yuxi, et autres
Publié: (2025)

Approximate Top-$k$ for Increased Parallelism
par: Key, Oscar, et autres
Publié: (2024)

Proactive Routing to Interpretable Surrogates with Distribution-Free Safety Guarantees
par: Uddin, Iqtedar, et autres
Publié: (2026)

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
par: Jaber, Edgar, et autres
Publié: (2024)

Learning to Defer: A Survey
par: Strong, Joshua, et autres
Publié: (2025)

Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR
par: Wu, Zelin, et autres
Publié: (2024)

Learning to Defer to a Population: A Meta-Learning Approach
par: Tailor, Dharmesh, et autres
Publié: (2024)

Density-Ratio Losses for Post-Hoc Learning to Defer
par: Soen, Alexander, et autres
Publié: (2026)

Fatigue-Aware Learning to Defer via Constrained Optimisation
par: Zhang, Zheng, et autres
Publié: (2026)

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
par: Athey, Susan, et autres
Publié: (2016)

Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
par: Liu, Hao, et autres
Publié: (2024)

Theoretical Convergence Guarantees for Variational Autoencoders
par: Surendran, Sobihan, et autres
Publié: (2024)

Top-$k$ Classification and Cardinality-Aware Prediction
par: Mao, Anqi, et autres
Publié: (2024)

Statistical Models of Top-$k$ Partial Orders
par: Awadelkarim, Amel, et autres
Publié: (2024)

Scalable and Precise Patch Robustness Certification for Deep Learning Models with Top-k Predictions
par: Zhou, Qilin, et autres
Publié: (2025)

A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees
par: Jamali, Mohammad Vahid, et autres
Publié: (2025)