6.2 KiB
| id | title | status | created | last_updated | author |
|---|---|---|---|---|---|
| 0002 | Parameter Tying Framework for GPy | Proposed | 2025-08-15 | 2025-08-15 | Neil Lawrence |
CIP-0002: Parameter Tying Framework for GPy
Status
Proposed - This CIP is open for community discussion and feedback.
Description
This CIP proposes to address the long-standing limitation in GPy's parameter system: the lack of systematic parameter tying capabilities. Parameter tying allows parameters across different kernel components to be constrained to have the same value, which is essential for many applications including LFM kernels, multi-task learning, and hierarchical models.
Motivation
Current Problem
- Missing Functionality: GPy lacks systematic parameter tying capabilities
- Long-standing Issue: This limitation has existed since the beginning of the project
- Broader Impact: Affects LFM kernels, multi-task learning, hierarchical models, and other multioutput scenarios
- User Requests: Multiple GitHub issues demonstrate user demand for this feature
Evidence from GitHub Issues
- GPy Issues: #462 (2016), #789 (2019), #878 (2020) - all requesting parameter tying functionality
- Paramz Issues: #34 (2019), #35 (2020) - confirming
tie_tois documented but not implemented - User Impact: Users resort to manual parameter management workarounds
Technical Context
- GPy depends on paramz (
"paramz>=0.9.6") for parameter management - Paramz framework has
tie_tomethod documented but not implemented - This is a fundamental limitation in the paramz framework that affects GPy
Potential Approaches
The community needs to discuss and evaluate different approaches to addressing parameter tying. Here are some initial thoughts to start the discussion:
Option 1: Fix Paramz Framework
Approach: Implement tie_to functionality in the paramz framework
- Pros: Addresses the root cause, benefits all paramz users
- Cons: Requires coordination with paramz maintainers, may be complex
- Effort: High - requires understanding paramz architecture
Option 2: GPy-Specific Workaround
Approach: Implement parameter tying within GPy without relying on paramz's missing features
- Pros: Can be implemented independently, focused on GPy needs
- Cons: Duplicates functionality that should exist in paramz
- Effort: Medium - requires careful integration with existing parameter system
Option 3: Replace Paramz Dependency
Approach: Migrate away from paramz to a different parameter management system
- Pros: Could address multiple paramz limitations
- Cons: Major architectural change, high risk and effort
- Effort: Very High - would require extensive refactoring
Option 4: [Community Input Needed]
Approach: [What other approaches should we consider?]
- Pros: [What are the advantages?]
- Cons: [What are the challenges?]
- Effort: [What level of effort would be required?]
Community Discussion Needed
We need community input to determine:
- Which approach(es) should we pursue?
- Are there other options we haven't considered?
- What are the relative priorities of different solutions?
- How should we balance immediate needs vs. long-term architectural improvements?
Implementation Plan
The implementation plan will be developed based on community feedback and the chosen approach. Some initial considerations:
Phase 1: Community Discussion
- Create GitHub issue to discuss parameter tying needs
- Engage with paramz maintainers about implementing
tie_to - Gather feedback from GPy users and maintainers
- Assess priority relative to other GPy improvements
- Determine which approach(es) to pursue
Phase 2: Technical Investigation
- Analyze chosen approach in detail
- Design parameter tying API and constraints
- Assess impact on existing GPy codebase
- Create proof-of-concept implementation
Phase 3: Implementation
- Implement chosen solution
- Create comprehensive test suite
- Update documentation and examples
- Demonstrate with LFM kernel use case
Some possible Acceptance Criteria
- Parameter tying functionality works for GPy kernels
- Support for equality constraints between parameters
- Integration with GPy's existing parameter system
- Comprehensive test coverage
- Documentation and examples
- Backward compatibility maintained
Risks and Considerations
Technical Risks
- Paramz Integration: Changes to paramz could break GPy
- Performance Impact: Parameter tying may affect optimization performance
- API Design: Need to design intuitive API for parameter constraints
Community Risks
- Maintainer Coordination: Requires coordination between GPy and paramz maintainers
- User Adoption: New API needs to be intuitive for existing users
- Priority Assessment: May compete with other GPy improvements
Related Work
- Backlog Item:
parameter-tying-framework- Investigation and CIP creation - CIP-0001: LFM kernel implementation (depends on parameter tying)
- GitHub Issues: #462, #789, #878 (GPy), #34, #35 (paramz)
- Paramz Framework: Current parameter management system
References
Discussion Points
We invite the community to discuss:
- Priority: How important is parameter tying relative to other GPy improvements?
- Approach: Which approach(es) should we pursue? Are there other options we haven't considered?
- Scope: What types of parameter constraints should be supported initially?
- API Design: What would be the most intuitive API for parameter tying?
- Timeline: What's a realistic timeline for implementation?
- Dependencies: How should we handle the relationship with paramz?
- Use Cases: What are the most important use cases for parameter tying beyond LFM kernels?
This CIP is open for community feedback and discussion. Please contribute your thoughts on the GitHub issue or through other community channels.