Cover Image

Information-Theoretic Framework for Understanding Modern Machine-Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feder, Meir, Urbanke, Ruediger, Fogel, Yaniv
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Information Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leave-One-Out Learning with Log-Loss
by: Fogel, Yaniv, et al.
Published: (2025)

Universal Batch Learning Under The Misspecification Setting
by: Vituri, Shlomi, et al.
Published: (2024)

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

Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective
by: Simeone, Osvaldo, et al.
Published: (2025)

Misspecified Universal Learning
by: Vituri, Shlomi, et al.
Published: (2026)

An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains
by: Zhou, Ruida, et al.
Published: (2024)

Federated One-Shot Learning with Data Privacy and Objective-Hiding
by: Egger, Maximilian, et al.
Published: (2025)

Context Channel Capacity: An Information-Theoretic Framework for Understanding Catastrophic Forgetting
by: Cheng, Ran
Published: (2026)

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
by: Laakom, Firas, et al.
Published: (2025)

Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures
by: Silva, Jorge F., et al.
Published: (2024)

Cryptanalysis via Machine Learning Based Information Theoretic Metrics
by: Kim, Benjamin D., et al.
Published: (2025)

Directed Information $γ$-covering: An Information-Theoretic Framework for Context Engineering
by: Huang, Hai
Published: (2025)

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

An Information-Theoretic Analysis of In-Context Learning
by: Jeon, Hong Jun, et al.
Published: (2024)

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

Machine Unlearning via Information Theoretic Regularization
by: Xu, Shizhou, et al.
Published: (2025)

Information Theoretic Learning for Diffusion Models with Warm Start
by: Shen, Yirong, et al.
Published: (2025)

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

An Information-Theoretic Framework for Out-of-Distribution Generalization with Applications to Stochastic Gradient Langevin Dynamics
by: Liu, Wenliang, et al.
Published: (2024)

An Effective Information Theoretic Framework for Channel Pruning
by: Chen, Yihao, et al.
Published: (2024)

Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
by: Bongole, Raghav, et al.
Published: (2024)

Information-Theoretic Discrete Diffusion
by: Jeon, Moongyu, et al.
Published: (2025)

Information-Theoretic Proofs for Diffusion Sampling
by: Reeves, Galen, et al.
Published: (2025)

An Information Theoretic Perspective on Conformal Prediction
by: Correia, Alvaro H. C., et al.
Published: (2024)

An Information-Theoretic Analysis of Temporal GNNs
by: Farzaneh, Amirmohammad
Published: (2024)

On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
by: Wu, Xuetong, et al.
Published: (2022)

On the Generalization of Knowledge Distillation: An Information-Theoretic View
by: Li, Bingying, et al.
Published: (2026)

A Theoretical Framework for Statistical Evaluability of Generative Models
by: Aiyer, Shashaank, et al.
Published: (2026)

An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
by: Hu, Dou, et al.
Published: (2025)

Unifying Information-Theoretic and Pair-Counting Clustering Similarity
by: Gates, Alexander J.
Published: (2025)

Information-Theoretic Measures on Lattices for High-Order Interactions
by: Liu, Zhaolu, et al.
Published: (2024)

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

Information-Theoretic State Variable Selection for Reinforcement Learning
by: Westphal, Charles, et al.
Published: (2024)

Bridging Algorithmic Information Theory and Machine Learning: A New Approach to Kernel Learning
by: Hamzi, Boumediene, et al.
Published: (2023)

Error Exponent in Agnostic PAC Learning
by: Hendel, Adi, et al.
Published: (2024)

On Theoretical Limits of Learning with Label Differential Privacy
by: Zhao, Puning, et al.
Published: (2025)

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

Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs
by: Huang, Dong, et al.
Published: (2024)

Forecastability as an Information-Theoretic Limit on Prediction
by: Catt, Peter Maurice
Published: (2026)

Interpolation of Quantum Polar Codes and Quantum Reed-Muller Codes
by: Hidaka, Keita, et al.
Published: (2025)