Imagen de Portada

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Montasser, Omar, Shao, Han, Abbe, Emmanuel
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Strategic Classification under Unknown Personalized Manipulation
por: Shao, Han, et al.
Publicado: (2023)

Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
por: Liao, Yufan, et al.
Publicado: (2022)

Bridging OOD Detection and Generalization: A Graph-Theoretic View
por: Wang, Han, et al.
Publicado: (2024)

On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
por: Pushkin, Denys, et al.
Publicado: (2024)

Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning
por: Zhang, Xinxun, et al.
Publicado: (2026)

An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
por: Liu, Xingtu
Publicado: (2025)

Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
por: Montasser, Omar, et al.
Publicado: (2025)

CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision
por: Altabaa, Awni, et al.
Publicado: (2025)

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes
por: Wang, Yu, et al.
Publicado: (2025)

Generalization on the Unseen, Logic Reasoning and Degree Curriculum
por: Abbe, Emmanuel, et al.
Publicado: (2023)

Sample-Adaptivity Tradeoff in On-Demand Sampling
por: Haghtalab, Nika, et al.
Publicado: (2025)

Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
por: Liao, Yufan, et al.
Publicado: (2023)

Derandomizing Multi-Distribution Learning
por: Larsen, Kasper Green, et al.
Publicado: (2024)

DDPM Score Matching and Distribution Learning
por: Chewi, Sinho, et al.
Publicado: (2025)

Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
por: Shen, Xu, et al.
Publicado: (2025)

Learning High-Degree Parities: The Crucial Role of the Initialization
por: Abbe, Emmanuel, et al.
Publicado: (2024)

Agnostic Multi-Robust Learning Using ERM
por: Ahmadi, Saba, et al.
Publicado: (2023)

How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
por: Abbe, Emmanuel, et al.
Publicado: (2024)

Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
por: Mahrooghi, Ilia, et al.
Publicado: (2026)

OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?
por: Jiang, Liangze, et al.
Publicado: (2024)

Theoretically Guaranteed Distribution Adaptable Learning
por: Xu, Chao, et al.
Publicado: (2024)

A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees
por: Jamali, Mohammad Vahid, et al.
Publicado: (2025)

$k$-server-bench: Automating Potential Discovery for the $k$-Server Conjecture
por: Brilliantov, Kirill, et al.
Publicado: (2026)

(Im)possibility of Automated Hallucination Detection in Large Language Models
por: Karbasi, Amin, et al.
Publicado: (2025)

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
por: Zuo, Shiyuan, et al.
Publicado: (2026)

The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks
por: Abbe, Emmanuel, et al.
Publicado: (2022)

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
por: Jin, Richeng, et al.
Publicado: (2020)

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
por: Wang, Cangqing, et al.
Publicado: (2024)

Statistical Learning Guarantees for Group-Invariant Barron Functions
por: Yang, Yahong, et al.
Publicado: (2025)

Boolformer: Symbolic Regression of Logic Functions with Transformers
por: d'Ascoli, Stéphane, et al.
Publicado: (2023)

Towards Robust Learning to Optimize with Theoretical Guarantees
por: Song, Qingyu, et al.
Publicado: (2025)

DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
por: Han, Xiaoxue, et al.
Publicado: (2024)

Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
por: Chowdhury, Mohammed Nowaz Rabbani, et al.
Publicado: (2026)

Theoretical Convergence Guarantees for Variational Autoencoders
por: Surendran, Sobihan, et al.
Publicado: (2024)

It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models
por: Xu, Xingcheng, et al.
Publicado: (2023)

Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression
por: Mao, Yixiu, et al.
Publicado: (2024)

MetaOOD: Automatic Selection of OOD Detection Models
por: Qin, Yuehan, et al.
Publicado: (2024)

Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
por: Duan, Chenguang, et al.
Publicado: (2024)

Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees
por: Banerjee, Bisakh, et al.
Publicado: (2025)

What If the Input is Expanded in OOD Detection?
por: Zhang, Boxuan, et al.
Publicado: (2024)