Learning General Sparse Additive Models from Point Queries in High Dimensions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00334998" target="_blank" >RIV/68407700:21340/19:00334998 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s00365-019-09461-6" target="_blank" >https://doi.org/10.1007/s00365-019-09461-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00365-019-09461-6" target="_blank" >10.1007/s00365-019-09461-6</a>

Result language
angličtina
Original language name
Learning General Sparse Additive Models from Point Queries in High Dimensions
Original language description
We consider the problem of learning a d-variate function f defined on the cube, where the algorithm is assumed to have black box access to samples of f within this domain. We then focus on the setting where f has an additive structure; i.e., it can be represented as a sparse sum of j-variate components, where j runs from zero to r. We derive randomized algorithms that query f at a carefully constructed set of points and exactly recover each component with high probability. In contrast to previous work, our analysis does not rely on numerical approximation of derivatives by finite order differences.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10101 - Pure mathematics

Project
<a href="/en/project/GA18-00580S" target="_blank" >GA18-00580S: Function Spaces and Approximation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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