Cover Image

Data-dependent Generalization Bounds via Variable-Size Compressibility

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sefidgaran, Milad, Zaidi, Abdellatif
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Information Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

Minimum Description Length and Generalization Guarantees for Representation Learning
by: Sefidgaran, Milad, et al.
Published: (2024)

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

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

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

Generalization Bounds via Conditional $f$-Information
by: Wang, Ziqiao, et al.
Published: (2024)

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
by: Aminian, Gholamali, et al.
Published: (2022)

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

Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
by: Pan, Zhixuan, et al.
Published: (2025)

Application of Deep Learning in Biological Data Compression
by: Zou, Chunyu
Published: (2025)

Comparing Comparators in Generalization Bounds
by: Hellström, Fredrik, et al.
Published: (2023)

Generalization Bounds for Transformer Channel Decoders
by: Zhang, Qinshan, et al.
Published: (2026)

Tighter Information-Theoretic Generalization Bounds via a Novel Class of Change of Measure Inequalities
by: Liu, Yanxiao, et al.
Published: (2026)

Generalization Bounds for Neural Belief Propagation Decoders
by: Adiga, Sudarshan, et al.
Published: (2023)

Block-Sample MAC-Bayes Generalization Bounds
by: Frey, Matthias, et al.
Published: (2026)

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
by: Kögler, Kevin, et al.
Published: (2024)

Fast Rate Information-theoretic Bounds on Generalization Errors
by: Wu, Xuetong, et al.
Published: (2023)

Generalization Bounds for Quantum Learning via Rényi Divergences
by: Warsi, Naqueeb Ahmad, et al.
Published: (2025)

A DPI-PAC-Bayesian Framework for Generalization Bounds
by: Guan, Muhan, et al.
Published: (2025)

Information-Theoretic Generalization Bounds for Deep Neural Networks
by: He, Haiyun, et al.
Published: (2024)

Efficient Learned Data Compression via Dual-Stream Feature Decoupling
by: Ma, Huidong, et al.
Published: (2026)

Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
by: Barnes, L. P., et al.
Published: (2022)

GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression
by: Talli, Pietro, et al.
Published: (2026)

Lossless Compression via Chained Lightweight Neural Predictors with Information Inheritance
by: Kim, Yuriy, et al.
Published: (2026)

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

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

Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
by: Kim, Kwangho, et al.
Published: (2026)

Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds
by: Yi, Xinping, et al.
Published: (2026)

MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression
by: Elias, Noel, et al.
Published: (2024)

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data
by: Heurtel-Depeiges, David, et al.
Published: (2024)

Learning to Compress: Local Rank and Information Compression in Deep Neural Networks
by: Patel, Niket, et al.
Published: (2024)

Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shift
by: Pan, Tianyu Bell, et al.
Published: (2025)

Data-Driven Estimation of Capacity Upper Bounds
by: Häger, Christian, et al.
Published: (2022)

Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency
by: Dai, Jincheng, et al.
Published: (2024)

dreaMLearning: Data Compression Assisted Machine Learning
by: Zhao, Xiaobo, et al.
Published: (2025)

Polynomial Context-Truncation Sensitivity in Autoregressive Language Models: Sequential Wyner-Ziv Bounds for KV Cache Compression
by: Kim, Munsik
Published: (2026)

Information-Theoretic Limits of Quantum Learning via Data Compression
by: Angrisani, Armando, et al.
Published: (2021)

Generalization analysis of an unfolding network for analysis-based Compressed Sensing
by: Kouni, Vicky, et al.
Published: (2023)