Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The supremum principle selects simple, transferable models   We consider how mathematical models enable predictions for conditions that
are qualitatively different from the training data. We propose techniques based
on information topology to find models that can apply their learning in regimes
for which there is no data. The first step is to use the Manifold Boundary
Approximation Method to construct simple, reduced models of target phenomena in
a data-driven way. We consider the set of all such reduced models and use the
topological relationships among them to reason about model selection for new,
unobserved phenomena. Given minimal models for several target behaviors, we
introduce the supremum principle as a criterion for selecting a new,
transferable model. The supremal model, i.e., the least upper bound, is the
simplest model that reduces to each of the target behaviors. We illustrate how
to discover supremal models with several examples; in each case, the supremal
model unifies causal mechanisms to transfer successfully to new target domains.
These examples culminate in a general algorithm that has formal connections to
theories of analogical reasoning in cognitive psychology.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con