Indirect Query Bayesian Optimization (IQBO)

Indirect Query Bayesian Optimization (IQBO) is a framework developed for a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of the function by adaptively querying and observing in the space transformed by the conditional distribution. This approach is motivated by real-world applications where direct feedback is inaccessible due to privacy, hardware, or computational constraints. The framework introduces the Conditional Max-Value Entropy Search (CMES) acquisition function to address this novel setting and proposes a hierarchical search algorithm to improve computational efficiency. Regret bounds for the proposed methods are shown, and the effectiveness of the approaches is demonstrated on simulated optimization tasks.

Category: Artificial Intelligence
Subcategory: Bayesian Optimization
Tags: Bayesian OptimizationIndirect QueryConditional ExpectationCMEShierarchical search
AI Type: Machine Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Optimization under privacy and computational constraints
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Bayesian Optimization

Model Architecture

Conditional Max-Value Entropy Search

Datasets Used

Simulated optimization tasks

Performance Metrics

Regret bounds

Deployment Options

Not specified

Cloud Based

No

On Premises

No

Features

Handles indirect feedback, improves computational efficiency

Enterprise

No

Hardware Requirements

Not specified

Supported Platforms

Not specified

Interoperability

Not specified

Security Features

Not specified

Compliance Standards

Not specified

Certifications

Not specified

Open Source

No

Source Code URL

http://Not specified

Documentation URL

http://Not specified

Community Support

Not specified

Contributors

Not specified

Training Data Size

Not specified

Inference Latency

Not specified

Energy Efficiency

Not specified

Explainability Features

Not specified

Ethical Considerations

Not specified

Known Limitations

Not specified

Industry Verticals

Not specified

Use Cases

Optimization in privacy-constrained environments

Customer Base

Not specified

Integration Options

Not specified

Scalability

Not specified

Support Options

Not specified

SLA

Not specified

User Interface

Not specified

Multi-Language Support

No

Localization

Not specified

Pricing Model

Not specified

Trial Availability

No

Partner Ecosystem

Not specified

Patent Information

Not specified

Regulatory Compliance

Not specified

Version

Not specified

Website URL

http://Not specified

Service Type

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Has API

No

API Details

Not specified

Business Model

Not specified

Price

0.00

Currency

Not specified

License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

Not specified

Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes