Imagen de Portada

Minimum Description Length and Generalization Guarantees for Representation Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Sefidgaran, Milad, Zaidi, Abdellatif, Krasnowski, Piotr
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Information Theory
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
por: Sefidgaran, Milad, et al.
Publicado: (2025)

Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
por: Sefidgaran, Milad, et al.
Publicado: (2025)

Minimal Communication-Cost Statistical Learning
por: Sefidgaran, Milad, et al.
Publicado: (2024)

Data-dependent Generalization Bounds via Variable-Size Compressibility
por: Sefidgaran, Milad, et al.
Publicado: (2023)

Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization
por: Sefidgaran, Milad, et al.
Publicado: (2025)

Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective
por: Kavian, Masoud, et al.
Publicado: (2025)

Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
por: Sefidgaran, Milad, et al.
Publicado: (2023)

A Theory of Machine Understanding via the Minimum Description Length Principle
por: Zhang, Canlin, et al.
Publicado: (2025)

Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization
por: Amaefuna, Theophilus, et al.
Publicado: (2026)

Equivalence of Privacy and Stability with Generalization Guarantees in Quantum Learning
por: Dasgupta, Ayanava, et al.
Publicado: (2026)

Bounds on the Excess Minimum Risk via Generalized Information Divergence Measures
por: Omanwar, Ananya, et al.
Publicado: (2025)

Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods
por: Taheri, Hossein, et al.
Publicado: (2024)

Minimum Entropy Coupling with Bottleneck
por: Ebrahimi, M. Reza, et al.
Publicado: (2024)

Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
por: Bermudez, Yaiza, et al.
Publicado: (2026)

Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
por: Taghipour, Milad, et al.
Publicado: (2025)

MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length
por: von Pichowski, Jan, et al.
Publicado: (2024)

Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
por: Janssen, Joseph, et al.
Publicado: (2022)

Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits
por: Bian, Jie, et al.
Publicado: (2024)

Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling
por: Nguyen, Nam, et al.
Publicado: (2026)

Information Theoretic Guarantees For Policy Alignment In Large Language Models
por: Mroueh, Youssef
Publicado: (2024)

Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks
por: Zhang, Shihao, et al.
Publicado: (2025)

Information Theoretic Perspective on Representation Learning
por: Pereg, Deborah, et al.
Publicado: (2026)

Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
por: Kim, Benjamin D., et al.
Publicado: (2026)

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
por: Dmitriev, Daniil, et al.
Publicado: (2026)

Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
por: Wu, Tong
Publicado: (2025)

Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression
por: Ioushua, Shahar Stein, et al.
Publicado: (2023)

Learning Robust Representations for Communications over Noisy Channels
por: Senthil, Sudharsan, et al.
Publicado: (2024)

Quad Length Codes for Lossless Compression of e4m3
por: Agrawal, Aditya, et al.
Publicado: (2026)

Learning Robust Representations for Communications over Interference-limited Channels
por: Paul, Shubham, et al.
Publicado: (2024)

Learning DNF through Generalized Fourier Representations
por: Heidari, Mohsen, et al.
Publicado: (2025)

Prediction-Powered Communication with Distortion Guarantees
por: Zecchin, Matteo, et al.
Publicado: (2025)

On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems
por: Yu, Yi, et al.
Publicado: (2026)

LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
por: Alikhani, Sadjad, et al.
Publicado: (2026)

Connecting Jensen-Shannon and Kullback-Leibler Divergences: A New Bound for Representation Learning
por: Dorent, Reuben, et al.
Publicado: (2025)

Performance Guarantees for Quantum Neural Estimation of Entropies
por: Sreekumar, Sreejith, et al.
Publicado: (2025)

An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
por: Pallewela, Rumeshika, et al.
Publicado: (2024)

The Representation Jensen-Shannon Divergence
por: Hoyos-Osorio, Jhoan K., et al.
Publicado: (2023)

Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements
por: Kızıldağ, Eren C.
Publicado: (2025)

The Generalization Error of Supervised Machine Learning Algorithms
por: Perlaza, Samir M., et al.
Publicado: (2024)

On the Generalization for Transfer Learning: An Information-Theoretic Analysis
por: Wu, Xuetong, et al.
Publicado: (2022)