Cover Image

On Transferring Transferability: Towards a Theory for Size Generalization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Levin, Eitan, Ma, Yuxin, Díaz, Mateo, Villar, Soledad
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Representation Theory Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Any-dimensional equivariant neural networks
by: Levin, Eitan, et al.
Published: (2023)

Any-Dimensional Invariant Universality
by: Yao, Shengtai, et al.
Published: (2026)

Invariant Kernels: Rank Stabilization and Generalization Across Dimensions
by: Díaz, Mateo, et al.
Published: (2025)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
by: Li, Gen, et al.
Published: (2023)

Wasserstein Transfer Learning
by: Zhang, Kaicheng, et al.
Published: (2025)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
by: Li, Gen, et al.
Published: (2020)

Source-Optimal Training is Transfer-Suboptimal
by: Hedges, C. Evans
Published: (2025)

Towards a unified framework for guided diffusion models
by: Jiao, Yuchen, et al.
Published: (2025)

Transfer Learning for Contextual Multi-armed Bandits
by: Cai, Changxiao, et al.
Published: (2022)

Robust Transfer Learning with Unreliable Source Data
by: Fan, Jianqing, et al.
Published: (2023)

Adjacency Spectral Embeddings of Correlation Networks
by: Levin, Keith
Published: (2026)

Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning
by: Prevost, Adrien, et al.
Published: (2025)

Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
by: Kim, Yeichan, et al.
Published: (2025)

Beyond Scores: Proximal Diffusion Models
by: Fang, Zhenghan, et al.
Published: (2025)

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
by: Wang, Yuyao, et al.
Published: (2025)

Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)

Transformers Meet In-Context Learning: A Universal Approximation Theory
by: Li, Gen, et al.
Published: (2025)

Identifiable Equivariant Networks are Layerwise Equivariant
by: Shahverdi, Vahid, et al.
Published: (2026)

Discovering Abstract Symbolic Relations by Learning Unitary Group Representations
by: Huh, Dongsung
Published: (2024)

Equivariant non-linear maps for neural networks on homogeneous spaces
by: Nyholm, Elias, et al.
Published: (2025)

Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
by: Sonoda, Sho, et al.
Published: (2024)

Transfer Learning for Nonparametric Contextual Dynamic Pricing
by: Wang, Fan, et al.
Published: (2025)

Tessellation Localized Transfer learning for nonparametric regression
by: Halconruy, Hélène, et al.
Published: (2026)

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
by: Shi, Laixi, et al.
Published: (2023)

Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
by: Cheng, Ziheng, et al.
Published: (2025)

Testing for correlation between network structure and high-dimensional node covariates
by: Fuchs-Kreiss, Alexander, et al.
Published: (2025)

Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023)

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators
by: Wang, Longlin, et al.
Published: (2025)

Transfer Learning for High-dimensional Quantile Regression with Distribution Shift
by: Bai, Ruiqi, et al.
Published: (2024)

Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
by: Zhang, Haobo, et al.
Published: (2024)

Adaptive Sample Aggregation In Transfer Learning
by: Hanneke, Steve, et al.
Published: (2024)

Asymptotic Theory of Eigenvectors for Latent Embeddings with Generalized Laplacian Matrices
by: Fan, Jianqing, et al.
Published: (2025)

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

Compact Matrix Quantum Group Equivariant Neural Networks
by: Pearce-Crump, Edward
Published: (2023)

Learning Transfer Operators by Kernel Density Estimation
by: Surasinghe, Sudam, et al.
Published: (2022)

Revisiting Theory of Contrastive Learning for Domain Generalization
by: Alvandi, Ali, et al.
Published: (2025)

The Generalized Skew Spectrum of Graphs
by: Bellante, Armando, et al.
Published: (2025)

A Unified Information-Theoretic Framework for Meta-Learning Generalization
by: Wen, Wen, et al.
Published: (2025)

Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
by: Hashimoto, Yuka, et al.
Published: (2025)

Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application
by: Li, Chenze, et al.
Published: (2025)