Overview
Explore robust and conjugate Gaussian process regression methods that maintain closed-form conditioning while overcoming limitations of standard GP assumptions about observation noise.
Syllabus
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- Introduction to Gaussian Processes
-- Overview of Gaussian processes
-- Gaussian process regression fundamentals
-- Standard assumptions about observation noise
- Challenges in Standard Gaussian Processes
-- Limitations of Gaussian noise assumptions
-- Real-world data challenges
-- The impact of non-Gaussian noise
- Robust Gaussian Process Regression
-- Definitions and concepts of robustness
-- Methods for robust GP regression
-- Handling heavy-tailed and non-Gaussian noise
-- Case studies and applications
- Conjugate Priors in Gaussian Processes
-- Definition and role of conjugacy in Bayesian methods
-- Benefits of conjugate priors in GP
-- Techniques for maintaining closed-form solutions
- Conjugate Gaussian Process Regression
-- Combining robustness with conjugate priors
-- Design of conjugate robust GP models
-- Algorithmic implementation
- Practical Applications
-- Examples in regression tasks
-- Comparison with standard GP models
-- Exploration of various datasets
- Computational Considerations
-- Scalability of robust and conjugate GP
-- Approximation methods for large datasets
-- Software and tools for implementation
- Case Studies and Projects
-- Analysis of state-of-the-art research
-- Group project on developing a robust GP model
-- Presentation of project findings
- Conclusion and Future Trends
-- Summary of key takeaways
-- Discussion on future research directions in robust GP
-- Emerging applications of GP regression
- Resources and Further Reading
-- Books, articles, and papers
-- Online courses and lectures
-- Open-source software libraries for GP regression
Taught by
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On-Demand
