Cover Image

An Information-Theoretic Criterion for Efficient Data Synthesis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Hanyu, Sun, Zhengqi, Deng, Xiaotie
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Information Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Information Criterion for Controlled Disentanglement of Multimodal Data
by: Wang, Chenyu, et al.
Published: (2024)

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

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

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

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

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

The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy
by: Emadi, Seyed Morteza
Published: (2026)

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

Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
by: Yang, Jiaming, et al.
Published: (2026)

Information-Theoretic Policy Pre-Training with Empowerment
by: Schneider, Moritz, et al.
Published: (2025)

Probing the Information Theoretical Roots of Spatial Dependence Measures
by: Wang, Zhangyu, et al.
Published: (2024)

Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
by: Zhang, Bohan, et al.
Published: (2025)

Laplace Sample Information: Data Informativeness Through a Bayesian Lens
by: Kaiser, Johannes, et al.
Published: (2025)

Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning
by: Wang, Zhongwei, et al.
Published: (2025)

An Information Theoretic Perspective on Agentic System Design
by: He, Shizhe, et al.
Published: (2025)

Partial Information Decomposition for Data Interpretability and Feature Selection
by: Westphal, Charles, et al.
Published: (2024)

Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
by: Kook, Sujin, et al.
Published: (2024)

Towards Efficient VLMs: Information-Theoretic Driven Compression via Adaptive Structural Pruning
by: Xu, Zhaoqi, et al.
Published: (2025)

A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
by: Zhang, Boyang, et al.
Published: (2025)

Will AI Trade? A Computational Inversion of the No-Trade Theorem
by: Li, Hanyu, et al.
Published: (2025)

The Agent Capability Problem: Predicting Solvability Through Information-Theoretic Bounds
by: Lutati, Shahar
Published: (2025)

Confidence-Based Decoding is Provably Efficient for Diffusion Language Models
by: Cai, Changxiao, et al.
Published: (2026)

Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation
by: Nichani, Arjun, et al.
Published: (2026)

Interpretable Diffusion via Information Decomposition
by: Kong, Xianghao, et al.
Published: (2023)

A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis
by: Bacha, Soufiane, et al.
Published: (2025)

Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models
by: Sharma, Jai, et al.
Published: (2026)

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

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
by: Yu, Youwei, et al.
Published: (2026)

Greedy Sampling Is Provably Efficient for RLHF
by: Wu, Di, et al.
Published: (2025)

General Information Metrics for Improving AI Model Training Efficiency
by: Xu, Jianfeng, et al.
Published: (2025)

Generalization and Informativeness of Conformal Prediction
by: Zecchin, Matteo, et al.
Published: (2024)

On the Limits of Self-Improving in Large Language Models: The Singularity Is Not Near Without Symbolic Model Synthesis
by: Zenil, Hector
Published: (2026)

Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding
by: Bhattacharjee, Payel, et al.
Published: (2025)

A Scalable Neural Network for DSIC Affine Maximizer Auction Design
by: Duan, Zhijian, et al.
Published: (2023)

Information-theoretic Bayesian Optimization: Survey and Tutorial
by: Garrido-Merchán, Eduardo C.
Published: (2025)

Semi-supervised Batch Learning From Logged Data
by: Aminian, Gholamali, et al.
Published: (2022)

Best Arm Identification with Possibly Biased Offline Data
by: Yang, Le, et al.
Published: (2025)

A Rational Account of Categorization Based on Information Theory
by: MacLellan, Christopher J., et al.
Published: (2026)

Entropy-informed Decoding: Adaptive Information-Driven Branching
by: Evans, Benjamin Patrick, et al.
Published: (2026)

Learning Invariant Graph Representations Through Redundant Information
by: Halder, Barproda, et al.
Published: (2025)