Explicit Context-adaptive Methods Cleanup

README.md

@@ -47,12 +47,13 @@ and opportunities in context-adaptive inference.
 5. [Principles of Context-Adaptive Inference](content/05.principles.md)
 6. [Explicit Adaptivity: Structured Estimation of $f(c)$](content/06.explicit.md)
 7. [Implicit Adaptivity: Emergent Contextualization within Complex Models](content/07.implicit.md)
-8. [Making Implicit Adaptivity Explicit: Local Models, Surrogates and Post Hoc Approximations](content/08.interpretations.md)
-9. [Context-Invariant Training: A View from the Converse](content/09.invariant.md)
-10. [Applications, Case Studies, and Software Tools](content/10.applications_tools.md)
-11. [Future Trends and Opportunities with Foundation Models](content/11.future_trends.md)
-12. [Open Problems](content/12.open_problems.md)
-13. [Conclusions](content/13.conclusion.md)
+8. [Evaluation and Design Principles for Context-Adaptive Inference](content/08.evaluation.md)
+9. [Making Implicit Adaptivity Explicit: Local Models, Surrogates and Post Hoc Approximations](content/09.interpretations.md)
+10. [Context-Invariant Training: A View from the Converse](content/10.invariant.md)
+11. [Applications, Case Studies, and Software Tools](content/11.applications_tools.md)
+12. [Future Trends and Opportunities with Foundation Models](content/12.future_trends.md)
+13. [Open Problems](content/13.open_problems.md)
+14. [Conclusions](content/14.conclusion.md)
 
 ## How can you contribute?
 We welcome contributions from the community. Please see our [contribution guidelines](CONTRIBUTING.md) for more information.

content/03.overview.md

@@ -67,13 +67,15 @@ Before diving into flexible estimators of $f(c)$, we review early modeling strat
 
 #### Conditional and Clustered Models
 
-One approach is to group observations into C contexts, either by manually defining conditions (e.g. male vs. female) or using unsupervised clustering. The partition can also be learned in a supervised, model-based way: model-based recursive partitioning fits a parametric model and splits the covariate space wherever its coefficients show the strongest instability, recursing to produce interpretable subgroups, with toolkits such as partykit providing standard implementations of these conditional-inference and model-based trees [@doi:10.1198/106186008X319331; @hothorn2015partykit]. Each group is then assigned a distinct parameter vector:
+One approach is to group observations into C contexts, either by manually defining conditions (e.g. male vs. female) or using unsupervised clustering. Each group is then assigned a distinct parameter vector:
 
 $$
 \{\widehat{\theta}_0, \ldots, \widehat{\theta}_C\} = \arg\max_{\theta_0, \ldots, \theta_C} \sum_{c \in \mathcal{C}} \ell(X_c; \theta_c),
 $$
 where $\ell(X; \theta)$ is the log-likelihood of $\theta$ on $X$ and $c$ specifies the covariate group that samples are assigned to. This reduces variance but limits granularity. It assumes that all members of a group share the same distribution and fails to capture variation within a group.
 
+Partitions can also be learned in a supervised, model-based way: model-based recursive partitioning fits a parametric model and splits the covariate space wherever its coefficients show the strongest instability, recursing to produce interpretable subgroups, with toolkits such as partykit providing standard implementations [@doi:10.1198/106186008X319331; @hothorn2015partykit].
+
 These early methods relax global homogeneity yet still rely on discrete partitions, motivating smoother and more flexible formulations explored in the next sections.
 
 #### Distance-Regularized Estimation
@@ -99,19 +101,20 @@ Semi-parametric VCMs relax the linearity assumption by requiring only that param
 
 ### Contextualized Models
 
-Contextualized models take a fully non-parametric approach, introduced in [@doi:10.48550/arXiv.1705.10301]. They assume that parameters are functions of context, $f(c)$, but do not restrict the form of $f$. Instead, $f$ is estimated directly, often with deep neural networks as function approximators:
+Contextualized models [@doi:10.48550/arXiv.2310.11340] take a fully non-parametric approach. They assume that parameters are functions of context, $f(c)$, but do not restrict the form of $f$. Instead, $f$ is estimated directly, often with deep neural networks as function approximators:
 $$
 \widehat{f} = \arg \max_{f \in \mathcal{F}} \sum_i \ell(x_i; f(c_i)).
 $$
-This framework has been widely applied, from machine learning toolboxes [@doi:10.48550/arXiv.2310.11340; @doi:10.21105/joss.06469] to personalized genomics [@doi:10.1101/2023.12.01.569658; @doi:10.48550/arXiv.2111.01104], biomedical informatics [@doi:10.48550/arXiv.2310.07918; @doi:10.1016/j.jbi.2022.104086; @doi:10.1101/2020.06.25.20140053], and contextual feature selection [@doi:10.48550/arXiv.2312.14254]. These examples highlight how contextual signals can drive adaptation without assuming a fixed functional form.
+This flexible approximation has enabled contextualized models to be applied to a wide range of across model types and application areas, including personalized disease analysis [@doi:10.1073/pnas.2411930122; @doi:10.48550/arXiv.2111.01104; @doi:10.1101/2020.06.25.20140053], heterogeneous treatment effect estimation [@doi:10.1016/j.jbi.2022.104086; @doi:10.48550/arXiv.2310.07918; @doi:10.48550/arXiv.2411.10645; @doi:10.48550/arXiv.1705.10301], drug development [@doi:10.64898/2026.05.11.724149], and contextual feature selection [@doi:10.48550/arXiv.2312.14254] and explainability [@doi:10.48550/arXiv.1705.10301]. These examples highlight how contextual signals can drive adaptation without assuming a fixed functional form. The contextualized.ml Python package provides standard implementations [@doi:10.21105/joss.06469]. 
 
 ### Partition and Latent-Structure Models
 
 Partition models extend the contextualized framework by assuming that parameters can be divided into homogeneous groups, while leaving group boundaries to be inferred. This design is useful for capturing abrupt changes over covariates such as time. Estimation typically balances the likelihood with a penalty on parameter differences between adjacent samples, often expressed through a Total Variation (TV) penalty [@doi:10.1214/09-AOAS308]:
 $$
 \{\widehat{\theta}_0, \dots, \widehat{\theta}_N\} = \arg\max_{\theta_0, \dots, \theta_N} \left( \sum_i \ell(x_i; \theta_i) + \lambda \sum_{i = 2}^N \|\theta_i - \theta_{i-1}\| \right).
 $$
-By encouraging piecewise-constant structures, partition models get closer to personalized modeling, balancing fit and parsimony, moving closer to personalized inference, trading off flexibility for interpretability. When the parameters being partitioned define a network, the support itself can jump across blocks, yielding varying-coefficient varying-structure models whose graph topology rewires with context, whether over time or along a branching biological lineage [@doi:10.1214/12-EJS739; @doi:10.1093/bioinformatics/btr239].
+By encouraging piecewise-constant structures, partition models get closer to personalized modeling, balancing fit and parsimony, moving closer to personalized inference, trading off flexibility for interpretability. 
+When the parameters being partitioned define a network, the support itself can jump across blocks, yielding varying-coefficient varying-structure models whose graph topology rewires with context, whether over time or along a branching biological lineage [@doi:10.1214/12-EJS739; @doi:10.1093/bioinformatics/btr239].
 
 ### Fine-tuned Models and Transfer Learning