[WIP] Outline TOC

[blengerich]

I will be outlining a table of contents to organize contributions.

[blengerich]

A proposed outline:

0. Front Matter

1. Abstract

2. Introduction

   • Purpose and Scope: Establishing the framework for our examination of context-adaptive statistical methods and the significance of foundation models.
   • Conceptual Foundations: Unpacking the core principles and historical impact of adaptive methods within statistical modeling.

3. Theoretical Foundations and Advances in Varying-Coefficient Models

   • Principles of Adaptivity: Analyzing the core principles that underpin adaptivity in statistical modeling.
   • Advances in Varying-Coefficient Models: Outlining key theoretical and methodological breakthroughs.
   • Integration with State-of-the-Art Machine Learning: Assessing the enhancement of VC models through modern ML technologies (e.g. deep learning, boosted trees, etc).

4. Opportunities for Foundation Models

   • Expanding Frameworks: Define foundation models, Explore how foundation models are redefining possibilities within statistical models.
   • Foundation models as context: Show recent progress and ongoing directions in using foundation models as context.

5. Applications, Case Studies, and Evaluations

   • Implementation Across Sectors: Detailed examination of context-adaptive models in sectors like healthcare and finance.
   • Performance Evaluation: Successes, failures, and comparative analyses of context-adaptive models across applications.

6. Technological and Software Tools

   • Survey of Tools: Reviewing current technological supports for context-adaptive models.
   • Selection and Usage Guidance: Offering practical advice on tool selection and utilization for optimal outcomes.

7. Future Trends and Predictions

   • Emerging Technologies: Identifying upcoming technologies and predicting their impact on context-adaptive learning.
   • Advances in Methodologies: Speculating on potential future methodological enhancements.

8. Open Problems

   • Theoretical Challenges: Critically examining unresolved theoretical issues like identifiability, etc.
   • Ethical and Regulatory Considerations: Discussing the ethical landscape and regulatory challenges, with focus on benefits of interpretability and regulatability.
   • Complexity in Implementation: Addressing obstacles in practical applications and gathering insights from real-world data.

9. Conclusion

   • Overview of Insights: Summarizing the main findings and contributions of this review.
   • Future Directions: Discussing potential developments and innovations in context-adaptive statistical inference.

[Sazan-Mahbub]

I can contribute in the FM related parts. In the current outline they are under Section 4.

[vexvexctor]

I can contribute to Implementation Across Sectors - under section 5

[cnellington]

I'll continue building Section 2.

[ethanwu2011]

I can contribute to "Theoretical Foundations and Advances in Varying-Coefficient Models - Integration with State-of-the-Art Machine Learning" as well as helping with the Healthcare-Related part of "implementation across sectors". I am also in general happy to help out where needed/open

[blengerich]

Since the general format of the TOC seems to be acceptable, I will be moving this into the main branch as a PR. We expect contributors to make an issue [WIP] Section A.B to indicate their plan of contributing a section, and then file a PR when a first draft of writing is ready for review. I will make issues for @Sazan-Mahbub , @vexvexctor , @cnellington , and @ethanwu2011 who have expressed interested in contributing sections here.