Copertina

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tian, Ye, Feng, Yang
Natura:	Preprint
Pubblicazione:	2021
Soggetti:	Machine Learning Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Neyman-Pearson multiclass classification under label noise via empirical likelihood
di: Zhang, Qiong, et al.
Pubblicazione: (2026)

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
di: Li, Michael Lingzhi, et al.
Pubblicazione: (2024)

Distribution-Free Rates in Neyman-Pearson Classification
di: Kalan, Mohammadreza M., et al.
Pubblicazione: (2024)

Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
di: Chen, Hanxiao, et al.
Pubblicazione: (2026)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
di: Noarov, Georgy, et al.
Pubblicazione: (2025)

Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
di: Tian, Ye, et al.
Pubblicazione: (2022)

Revamping Conformal Selection With Optimal Power: A Neyman--Pearson Perspective
di: Qin, Jing, et al.
Pubblicazione: (2025)

Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
di: Fan, Jianqing, et al.
Pubblicazione: (2023)

Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
di: Kalan, Mohammadreza M., et al.
Pubblicazione: (2025)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
di: Kato, Masahiro
Pubblicazione: (2026)

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2024)

Transfer Neyman-Pearson Algorithm for Outlier Detection
di: Kalan, Mohammadreza M., et al.
Pubblicazione: (2025)

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds
di: Chen, Aoran, et al.
Pubblicazione: (2025)

Goodness of fit by Neyman-Pearson testing
di: Grosso, Gaia, et al.
Pubblicazione: (2023)

Robust and Adaptive Spectral Method for Representation Multi-Task Learning with Contamination
di: Huang, Yian, et al.
Pubblicazione: (2025)

Bounding Neyman-Pearson Region with $f$-Divergences
di: Mullhaupt, Andrew, et al.
Pubblicazione: (2025)

Beyond Neyman-Pearson: e-values enable hypothesis testing with a data-driven alpha
di: Grünwald, Peter
Pubblicazione: (2022)

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings
di: Sui, Yang, et al.
Pubblicazione: (2025)

Federated Transfer Learning with Differential Privacy
di: Li, Mengchu, et al.
Pubblicazione: (2024)

Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
di: Liu, Yuxin, et al.
Pubblicazione: (2026)

MLCBART: Multilabel Classification with Bayesian Additive Regression Trees
di: Tian, Jiahao, et al.
Pubblicazione: (2026)

Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
di: Liu, Lingchong, et al.
Pubblicazione: (2025)

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
di: Fan, Qingliang, et al.
Pubblicazione: (2025)

A cost-sensitive constrained Lasso
di: Blanquero, Rafael, et al.
Pubblicazione: (2024)

Extended Isolation Forest with feature sensitivities
di: Donhauzer, Illia
Pubblicazione: (2026)

Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness
di: Hou, Xiaotian, et al.
Pubblicazione: (2024)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
di: Liu, Siyan, et al.
Pubblicazione: (2024)

Multi forests: Variable importance for multi-class outcomes
di: Hornung, Roman, et al.
Pubblicazione: (2024)

Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data
di: Tian, Ye, et al.
Pubblicazione: (2024)

A new paradigm for global sensitivity analysis
di: Mazo, Gildas
Pubblicazione: (2024)

Mining Invariance from Nonlinear Multi-Environment Data: Binary Classification
di: Goddard, Austin, et al.
Pubblicazione: (2024)

Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
di: Ying, Chao, et al.
Pubblicazione: (2025)

Doubly Robust Fusion of Many Treatments for Policy Learning
di: Zhu, Ke, et al.
Pubblicazione: (2025)

Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization
di: Cui, Ying, et al.
Pubblicazione: (2026)

Classification Under Local Differential Privacy with Model Reversal and Model Averaging
di: Qin, Caihong, et al.
Pubblicazione: (2026)

Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion
di: Li, Yi, et al.
Pubblicazione: (2024)

Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification
di: Chen, Jiachen, et al.
Pubblicazione: (2024)

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks
di: Leonelli, Manuele, et al.
Pubblicazione: (2024)

Multi-Task Learning with Summary Statistics
di: Knight, Parker, et al.
Pubblicazione: (2023)

Stable Causal Discovery via Directed Acyclic Graph Aggregation
di: Wu, Yunan, et al.
Pubblicazione: (2026)