:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Yuanchao, Lai, Zhao-Rong, Zhong, Tianqi
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2502.19665
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Environment-Conditioned Tail Reweighting for Total Variation Invariant Risk Minimization
di: Wang, Yuanchao, et al.
Pubblicazione: (2026)

Invariant Risk Minimization Is A Total Variation Model
di: Lai, Zhao-Rong, et al.
Pubblicazione: (2024)

Out-of-Distribution Optimality of Invariant Risk Minimization
di: Toyota, Shoji, et al.
Pubblicazione: (2023)

Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
di: Wang, Song, et al.
Pubblicazione: (2025)

Tree-based Ensemble Learning for Out-of-distribution Detection
di: Shen, Zhaiming, et al.
Pubblicazione: (2024)

Invariant Graph Transformer for Out-of-Distribution Generalization
di: Liao, Tianyin, et al.
Pubblicazione: (2025)

Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
di: Norman, Yotam, et al.
Pubblicazione: (2025)

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration
di: Yoshida, Kotaro, et al.
Pubblicazione: (2024)

Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors
di: Majumdar, Anirudha
Pubblicazione: (2025)

Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training
di: Yang, Xi, et al.
Pubblicazione: (2025)

BootOOD: Self-Supervised Out-of-Distribution Detection via Synthetic Sample Exposure under Neural Collapse
di: Wang, Yuanchao, et al.
Pubblicazione: (2025)

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization
di: Schapiro, Samuel, et al.
Pubblicazione: (2024)

Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
di: Mao, Wenyu, et al.
Pubblicazione: (2024)

A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization
di: Bakker, Hua Chang, et al.
Pubblicazione: (2024)

Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
di: Zhang, Simon, et al.
Pubblicazione: (2026)

Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
di: Liu, Haoxin, et al.
Pubblicazione: (2024)

Feature Protection For Out-of-distribution Generalization
di: Tan, Lu, et al.
Pubblicazione: (2024)

Improving Graph Out-of-distribution Generalization Beyond Causality
di: Xu, Can, et al.
Pubblicazione: (2024)

Data-Driven Generation of Neutron Star Equations of State Using Variational Autoencoders
di: Ross, Alex, et al.
Pubblicazione: (2026)

FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
di: Liao, Xinting, et al.
Pubblicazione: (2024)

Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
di: Liu, Yibing, et al.
Pubblicazione: (2023)

Can In-context Learning Really Generalize to Out-of-distribution Tasks?
di: Wang, Qixun, et al.
Pubblicazione: (2024)

Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
di: Zhang, Songming, et al.
Pubblicazione: (2023)

diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs
di: Mo, Zhaobin, et al.
Pubblicazione: (2024)

Analysis of Total Variation Minimization for Clustered Federated Learning
di: Jung, A.
Pubblicazione: (2024)

Topology-aware Robust Optimization for Out-of-distribution Generalization
di: Qiao, Fengchun, et al.
Pubblicazione: (2023)

Spurious Feature Diversification Improves Out-of-distribution Generalization
di: Lin, Yong, et al.
Pubblicazione: (2023)

GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
di: Chen, Jinggang, et al.
Pubblicazione: (2023)

Universally Invariant Learning in Equivariant GNNs
di: Cen, Jiacheng, et al.
Pubblicazione: (2025)

Linear Trading Position with Sparse Spectrum
di: Lai, Zhao-Rong, et al.
Pubblicazione: (2025)

Variational f-divergence Minimization
di: Zhang, Mingtian, et al.
Pubblicazione: (2019)

BOOD: Boundary-based Out-Of-Distribution Data Generation
di: Liao, Qilin, et al.
Pubblicazione: (2025)

Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
di: Li, Bingcong, et al.
Pubblicazione: (2024)

Functional Risk Minimization
di: Alet, Ferran, et al.
Pubblicazione: (2024)

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
di: Zhao, Tianqi, et al.
Pubblicazione: (2024)

Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
di: Tieu, Katherine, et al.
Pubblicazione: (2025)

Mining In-distribution Attributes in Outliers for Out-of-distribution Detection
di: Lei, Yutian, et al.
Pubblicazione: (2024)

KAN based Autoencoders for Factor Models
di: Wang, Tianqi, et al.
Pubblicazione: (2024)

Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data
di: Tao, Lan, et al.
Pubblicazione: (2024)

Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
di: Yang, Shenzhi, et al.
Pubblicazione: (2025)