Mutual Information (MI) is a measure of the dependency between variables, crucial for various applications in machine learning. However, computing MI in high-dimensional spaces with intractable likelihoods is challenging. This paper presents a Bayesian nonparametric (BNP) framework for robust MI estimation, using a finite representation of the Dirichlet process posterior to incorporate regularization. This approach reduces sensitivity to fluctuations and outliers, especially in small sample settings, and improves convergence of the MI approximation. The framework is applied to maximize MI between data and latent spaces in variational autoencoders, showing significant improvements in convergence and structure discovery.
Dirichlet Process, Variational Inference
Bayesian Nonparametric Models
Synthetic and real datasets for MI estimation
Convergence Rate, Robustness
Cloud-based, On-premises
Yes
Yes
Robust, Scalable, High-dimensional
Yes
Standard computing resources
Linux, Windows, macOS
Compatible with various statistical software
Data privacy, Secure computation
GDPR
None
Yes
Active research community
Statisticians, Data Scientists
Gigabytes
Moderate
High
High
Data privacy
Complexity in high dimensions
Finance, Healthcare, Telecommunications
Feature selection, Dependency analysis, Anomaly detection
Research institutions, Data-driven companies
APIs, SDKs
Scalable
Community forums, Technical support
99.9% uptime
Command-line, Web-based
Yes
Available in multiple languages
Open-source
Yes
Academic partners
None
Compliant with major regulations
1.0
Open-source software
Yes
RESTful API
Open-source
0.00
USD
MIT
01/03/2023
01/10/2023
+1-800-555-0199
Supports high-dimensional data, Regularization techniques
Yes