:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Canlin, Liu, Xiuwen
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Information Theory
Online Access:	https://arxiv.org/abs/2405.11684
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
by: Zhang, Canlin, et al.
Published: (2023)

Orthogonal Approximate Message Passing with Optimal Spectral Initializations for Rectangular Spiked Matrix Models
by: Chen, Haohua, et al.
Published: (2025)

Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm
by: Li, Jingyang, et al.
Published: (2024)

Orthogonal Approximate Message Passing Algorithms for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
by: Chen, Haohua, et al.
Published: (2026)

Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions
by: Kirsch, Andreas
Published: (2024)

A Generalized Information Bottleneck Theory of Deep Learning
by: Westphal, Charles, et al.
Published: (2025)

Optimality of Approximate Message Passing Algorithms for Spiked Matrix Models with Rotationally Invariant Noise
by: Dudeja, Rishabh, et al.
Published: (2024)

Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects
by: Alrashdi, Ayed M., et al.
Published: (2026)

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

A Unified Fractional Regularization Framework for Sparse Recovery
by: Zhao, Yinhao, et al.
Published: (2026)

Refining Labeling Functions with Limited Labeled Data
by: Li, Chenjie, et al.
Published: (2025)

Optimal Differentially Private PCA and Estimation for Spiked Covariance Matrices
by: Cai, T. Tony, et al.
Published: (2024)

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

On the Theory of Continual Learning with Gradient Descent for Neural Networks
by: Taheri, Hossein, et al.
Published: (2025)

Empirical Risk Minimization with Relative Entropy Regularization
by: Perlaza, Samir M., et al.
Published: (2022)

Asymmetry of the Relative Entropy in the Regularization of Empirical Risk Minimization
by: Daunas, Francisco, et al.
Published: (2024)

The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning
by: Wu, Youqi, et al.
Published: (2026)

Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application
by: Ramírez, Camilo, et al.
Published: (2024)

Learning Causality for Longitudinal Data
by: Bouchattaoui, Mouad EL
Published: (2025)

Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
by: Daunas, Francisco, et al.
Published: (2024)

Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks
by: Faramarzi, Sajad, et al.
Published: (2024)

OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data
by: Zhao, Yan, et al.
Published: (2026)

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

Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of Automatic Relevance Determination
by: Yoshida, Tsukasa, et al.
Published: (2025)

A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings
by: Agarwal, Sidharth, et al.
Published: (2025)

A Constrained BA Algorithm for Rate-Distortion and Distortion-Rate Functions
by: Chen, Lingyi, et al.
Published: (2023)

A Mathematical Theory for Learning Semantic Languages by Abstract Learners
by: Liao, Kuo-Yu, et al.
Published: (2024)

Output-Constrained Lossy Source Coding With Application to Rate-Distortion-Perception Theory
by: Xie, Li, et al.
Published: (2024)

Neuromorphic Wireless Split Computing with Multi-Level Spikes
by: Wu, Dengyu, et al.
Published: (2024)

Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
by: Chaabouni, Youssef, et al.
Published: (2026)

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
by: Bibas, Koby
Published: (2024)

Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication
by: Zhou, Yujie, et al.
Published: (2025)

Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification
by: Bereyhi, Ali, et al.
Published: (2025)

Automatic Regularization for Linear MMSE Filters
by: Zanco, Daniel Gomes de Pinho, et al.
Published: (2023)

ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling
by: Wang, Shun, et al.
Published: (2025)

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

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

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

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
by: Huang, Alyssa, et al.
Published: (2023)