4th International Conference on Information Theory and Machine Learning (ITEORY 2026))

Scope & Topics

4^th International Conference on Information Theory and Machine Learning (ITEORY 2026) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Information Theory. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the area soft Information Theory, Applications and Machine Learning.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science, Engineering and Applications.

Topics of interest include, but are not limited to, the following:

Shannon Theory and Fundamental Limits
Finite Blocklength and High Dimensional Information Theory
Network and Multi User Information Theory
Information Geometry and Divergence Measures
Advanced Coding, Coded Modulation and Neural Compression
Sequence Design, Data Compression and Sampling Theory
Information Theoretic Cryptography and Complexity
Quantum Information Theory, Quantum Coding and Thermodynamics
DNA Storage, Genomics and Biological Information Theory
AI Native Waveform Design and Integrated Sensing Communication
Semantic and Goal Oriented Communications
Deep Learning for Communications and Wireless Embodied Intelligence
Optical, Wireless, IoT and TE QKD Systems
Information Theoretic Machine Learning
Learning, Inference and Bayesian Information Theory
Information Theory for Generative AI and Diffusion Models
Multimodal Information Theory and Foundation Model Alignment
Representation Learning and Information Bottleneck
Information Theoretic AI Safety, Robustness and Privacy
Information Theory for Large Language Models (LLMs)
Information Theory for Foundation Models and Scaling Laws
Information Theory for RAG and Memory Systems
Information Theory for Edge Deployed LLMs
Federated Learning and Federated Foundation Models
Information Theory for Graph Neural Networks
Information Theory for Reinforcement Learning
Information Theory for Neuromorphic and Spiking AI
Information Theory for Synthetic Data and Data Generation
Information Theory for Digital Twins and Cyber Physical Systems
Information Theory for Autonomous and Intelligent Systems
Information Theory for Time Series and Dynamical Systems

Paper Submission

Authors are invited to submit papers through the conference Submission System by August 15, 2026 .

Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

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