Cover Image

A Sufficient-Statistic Reduction of the Information Bottleneck to a Low-Dimensional Problem

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Armstrong, Joss
Format:	Preprint
Published:	2026
Subjects:	Information Theory Machine Learning 94A17 (Primary) 60B99, 62B10 (Secondary) F.2.2; I.2.6; G.3
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generalized Maximum Entropy: When and Why you need it
by: Ferro, Giuseppe M., et al.
Published: (2025)

MISES: Minimal Information Sufficiency for Effective Service
by: Armstrong, Joss
Published: (2026)

Submodular Benchmark Selection
by: Smola, Alexander
Published: (2026)

Kernel Integrated $R^2$: A Measure of Dependence
by: Roudaki, Pouya, et al.
Published: (2026)

On the Accuracy of Newton Step and Influence Function Data Attributions
by: Rubinstein, Ittai, et al.
Published: (2025)

Robustness Auditing for Linear Regression: To Singularity and Beyond
by: Rubinstein, Ittai, et al.
Published: (2024)

A Fiber Criterion for Representation Identifiability in Supervised Learning
by: Sevetlidis, Vasileios
Published: (2026)

Feature Learning in Wide Neural Networks under $μ$P: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit
by: Xodarev, Akmal
Published: (2026)

Generalization and Feature Attribution in Machine Learning Models for Crop Yield and Anomaly Prediction in Germany
by: Baatz, Roland
Published: (2025)

Human Supervision as an Information Bottleneck: A Unified Theory of Error Floors in Human-Guided Learning
by: Dominguez, Alejandro Rodriguez
Published: (2026)

A Unifying Framework for Unsupervised Concept Extraction
by: Squires, Chandler, et al.
Published: (2026)

Nyström Kernel Stein Discrepancy Tests
by: Kalinke, Florian, et al.
Published: (2026)

Quantum computer formulation of the FKP-operator eigenvalue problem for probabilistic learning on manifolds
by: Soize, Christian, et al.
Published: (2025)

Autoencoders in Function Space
by: Bunker, Justin, et al.
Published: (2024)

Learning Real-Life Approval Elections
by: Faliszewski, Piotr, et al.
Published: (2026)

Recursive Meta-Distillation: An Axiomatic Framework for Iterative Knowledge Refinement
by: Flouro, Aaron R., et al.
Published: (2026)

Towards Verifiable AI with Lightweight Cryptographic Proofs of Inference
by: Anchuri, Pranay, et al.
Published: (2026)

Score-based Diffusion Models in Function Space
by: Lim, Jae Hyun, et al.
Published: (2023)

A Physics-Informed Variational Inference Framework for Identifying Attributions of Extreme Stress Events in Low-Grain Polycrystals
by: Zhang, Yinling, et al.
Published: (2025)

A Multi-Evidence Framework Rescues Low-Power Prognostic Signals and Rejects Statistical Artifacts in Cancer Genomics
by: Akarlar, Gokturk Aytug
Published: (2025)

The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence
by: Cai, Yichao, et al.
Published: (2026)

Categorical Invariants of Learning Dynamics
by: Tamim, Abdulrahman
Published: (2025)

Alignment Verifiability in Large Language Models: Normative Indistinguishability under Behavioral Evaluation
by: Santos-Grueiro, Igor
Published: (2026)

Caterpillar GNN: Replacing Message Passing with Efficient Aggregation
by: Černý, Marek
Published: (2025)

Closed-Form Beta Distribution Estimation from Sparse Statistics with Random Forest Implicit Regularization
by: Landers, Jonathan R.
Published: (2025)

Neural Methods for Multiple Systems Estimation Models
by: Marsh, Joseph, et al.
Published: (2026)

Nyström Kernel Stein Discrepancy
by: Kalinke, Florian, et al.
Published: (2024)

The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
by: Cribeiro-Ramallo, Jose, et al.
Published: (2025)

Nearly Tight Lower Bounds for Relaxed Locally Decodable Codes via Robust Daisies
by: Goldberg, Guy, et al.
Published: (2025)

Intelligence Inertia: Physical Isomorphism and Applications
by: Han, Jipeng
Published: (2026)

Structural Properties of Entropic Vectors and Stability of the Ingleton Inequality
by: Matveev, Rostislav, et al.
Published: (2025)

Central Limit Theorems for Transition Probabilities of Controlled Markov Chains
by: Su, Ziwei, et al.
Published: (2025)

Thermodynamic Regulation of Finite-Time Gibbs Training in Energy-Based Models: A Restricted Boltzmann Machine Study
by: Süleymanoğlu, Görkem Can
Published: (2026)

Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints
by: Kasaura, Kazumi
Published: (2024)

Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning
by: Stricker, Patrick, et al.
Published: (2026)

Attribution Projection Calculus: A Novel Framework for Causal Inference in Bayesian Networks
by: Amin, M Ruhul
Published: (2025)

Optimal Online Change Detection via Random Fourier Features
by: Kalinke, Florian, et al.
Published: (2025)

How Does Machine Learning Manage Complexity?
by: Fortnow, Lance
Published: (2026)

Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
by: Høyheim, Eirik, et al.
Published: (2025)

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
by: Hubin, Aliaksandr
Published: (2025)