Imagen de Portada

Transformers Learn Robust In-Context Regression under Distributional Uncertainty

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Cao, Hoang T. H., Trinh, Hai D. V., Quan, Tho, Truong, Lan V.
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Transformers Don't In-Context Learn Least Squares Regression
por: Hill, Joshua, et al.
Publicado: (2025)

Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
por: Xu, Xinran, et al.
Publicado: (2026)

How Transformers Learn In-Context Recall Tasks? Optimality, Training Dynamics and Generalization
por: Nguyen, Quan, et al.
Publicado: (2025)

Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
por: Li, Yanjie, et al.
Publicado: (2024)

Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
por: Nguyen, Nang Hung, et al.
Publicado: (2026)

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
por: Cui, Mingxuan, et al.
Publicado: (2025)

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
por: Sovrano, Francesco, et al.
Publicado: (2026)

Detecting Neurovascular Instability from Multimodal Physiological Signals Using Wearable-Compatible Edge AI: A Responsible Computational Framework
por: Hoa, Truong Quynh, et al.
Publicado: (2026)

On the Robustness of Transformers against Context Hijacking for Linear Classification
por: Li, Tianle, et al.
Publicado: (2025)

Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization
por: Nguyen, Anh B. H., et al.
Publicado: (2026)

Robust Regularized Policy Iteration under Transition Uncertainty
por: Lin, Hongqiang, et al.
Publicado: (2026)

Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty
por: Ozeki, Ren, et al.
Publicado: (2026)

Uncertainty Regularized Evidential Regression
por: Ye, Kai, et al.
Publicado: (2024)

Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
por: Lin, Tianjue, et al.
Publicado: (2026)

Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
por: Fu, Deqing, et al.
Publicado: (2023)

UncertaintyZoo: A Unified Toolkit for Quantifying Predictive Uncertainty in Deep Learning Systems
por: Wu, Xianzong, et al.
Publicado: (2025)

Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
por: Micheli, Francesco, et al.
Publicado: (2025)

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
por: Yang, Rui, et al.
Publicado: (2024)

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
por: Chen, Xingwu, et al.
Publicado: (2025)

Efficient Modular Learning through Naive LoRA Summation: Leveraging Orthogonality in High-Dimensional Models
por: Cao, Zhanhao, et al.
Publicado: (2025)

Transformers Handle Endogeneity in In-Context Linear Regression
por: Liang, Haodong, et al.
Publicado: (2024)

Graph Diffusion Transformers are In-Context Molecular Designers
por: Liu, Gang, et al.
Publicado: (2025)

Investigation into In-Context Learning Capabilities of Transformers
por: Chandrupatla, Rushil, et al.
Publicado: (2026)

Learning under Quantization for High-Dimensional Linear Regression
por: Zhang, Dechen, et al.
Publicado: (2025)

Revisiting Kernel Attention with Correlated Gaussian Process Representation
por: Bui, Long Minh, et al.
Publicado: (2025)

Adaptive Conformal Guidance for Learning under Uncertainty
por: Liu, Rui, et al.
Publicado: (2025)

Adjusting Regression Models for Conditional Uncertainty Calibration
por: Gao, Ruijiang, et al.
Publicado: (2024)

Few-Shot Class Incremental Learning via Robust Transformer Approach
por: Paeedeh, Naeem, et al.
Publicado: (2024)

Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis
por: Du, Yuchen, et al.
Publicado: (2024)

SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
por: Le, Trung Hoang, et al.
Publicado: (2025)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
por: Christodoulou, Leonidas, et al.
Publicado: (2026)

Uncertainty Estimation using Variance-Gated Distributions
por: Gillis, H. Martin, et al.
Publicado: (2025)

Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
por: Yang, Zewen, et al.
Publicado: (2024)

Long-Context Attention Benchmark: From Kernel Efficiency to Distributed Context Parallelism
por: Bu, Tao, et al.
Publicado: (2025)

Norm-Hierarchy Transitions in Representation Learning: When and Why Neural Networks Abandon Shortcuts
por: Khanh, Truong Xuan, et al.
Publicado: (2026)

Meta-Learning Transformers to Improve In-Context Generalization
por: Braccaioli, Lorenzo, et al.
Publicado: (2025)

Learning to Solve Job Shop Scheduling under Uncertainty
por: Infantes, Guillaume, et al.
Publicado: (2024)

Unimodal Distributions for Ordinal Regression
por: Cardoso, Jaime S., et al.
Publicado: (2023)

Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds
por: Liang, Hao, et al.
Publicado: (2022)

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks
por: Li, Chengxi, et al.
Publicado: (2025)