Matches in SemOpenAlex for { <https://semopenalex.org/work/W3165092086> ?p ?o ?g. }
- W3165092086 abstract "We propose a novel active learning strategy for regression, which is model-agnostic, robust against model mismatch, and interpretable. Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as thelocally optimal bandwidths (LOB) -- the bandwidth function that minimizes MISE in the asymptotic limit. The asymptotic expression of our objective then reveals the dependence of the MISE on the training density, enabling analytic minimization. As a result, we obtain the optimal training density in a closed-form. The almost model-free nature of our approach should encode raw properties of the target problem, and thus provide a robust and model-agnostic active learning strategy. Furthermore, the obtained training density factorizes the influence of local function complexity, noise leveland test density in a transparent and interpretable way. We validate our theory in numerical simulations, and show that the proposed active learning method outperforms the existing state-of-the-art model-agnostic approaches." @default.
- W3165092086 created "2021-06-07" @default.
- W3165092086 creator A5034738514 @default.
- W3165092086 creator A5065845667 @default.
- W3165092086 creator A5072994165 @default.
- W3165092086 date "2021-05-25" @default.
- W3165092086 modified "2023-10-18" @default.
- W3165092086 title "Optimal Sampling Density for Nonparametric Regression." @default.
- W3165092086 cites W1484084878 @default.
- W3165092086 cites W1589836740 @default.
- W3165092086 cites W1591699755 @default.
- W3165092086 cites W1866842065 @default.
- W3165092086 cites W1975285668 @default.
- W3165092086 cites W1978617705 @default.
- W3165092086 cites W2001213989 @default.
- W3165092086 cites W2016213767 @default.
- W3165092086 cites W2017977879 @default.
- W3165092086 cites W2026386069 @default.
- W3165092086 cites W2038751902 @default.
- W3165092086 cites W2057032881 @default.
- W3165092086 cites W2059507684 @default.
- W3165092086 cites W2076118331 @default.
- W3165092086 cites W2080021732 @default.
- W3165092086 cites W2085989833 @default.
- W3165092086 cites W2090914356 @default.
- W3165092086 cites W2093381735 @default.
- W3165092086 cites W2104053192 @default.
- W3165092086 cites W2115305054 @default.
- W3165092086 cites W2118021080 @default.
- W3165092086 cites W2130941826 @default.
- W3165092086 cites W2138079527 @default.
- W3165092086 cites W2141195893 @default.
- W3165092086 cites W2142275721 @default.
- W3165092086 cites W2150568880 @default.
- W3165092086 cites W2158006878 @default.
- W3165092086 cites W2158940042 @default.
- W3165092086 cites W2160828669 @default.
- W3165092086 cites W2165618135 @default.
- W3165092086 cites W2171277043 @default.
- W3165092086 cites W2480210587 @default.
- W3165092086 cites W2505990019 @default.
- W3165092086 cites W2798820905 @default.
- W3165092086 cites W2809113079 @default.
- W3165092086 cites W2890546558 @default.
- W3165092086 cites W2903158431 @default.
- W3165092086 cites W2908510526 @default.
- W3165092086 cites W2945208055 @default.
- W3165092086 cites W2963199592 @default.
- W3165092086 cites W2963902936 @default.
- W3165092086 cites W3035559885 @default.
- W3165092086 cites W3099276598 @default.
- W3165092086 cites W3105521089 @default.
- W3165092086 cites W3111084079 @default.
- W3165092086 cites W3112014425 @default.
- W3165092086 cites W591273972 @default.
- W3165092086 cites W867138012 @default.
- W3165092086 hasPublicationYear "2021" @default.
- W3165092086 type Work @default.
- W3165092086 sameAs 3165092086 @default.
- W3165092086 citedByCount "0" @default.
- W3165092086 crossrefType "posted-content" @default.
- W3165092086 hasAuthorship W3165092086A5034738514 @default.
- W3165092086 hasAuthorship W3165092086A5065845667 @default.
- W3165092086 hasAuthorship W3165092086A5072994165 @default.
- W3165092086 hasConcept C105795698 @default.
- W3165092086 hasConcept C107321475 @default.
- W3165092086 hasConcept C119857082 @default.
- W3165092086 hasConcept C126255220 @default.
- W3165092086 hasConcept C134306372 @default.
- W3165092086 hasConcept C152877465 @default.
- W3165092086 hasConcept C177148314 @default.
- W3165092086 hasConcept C185429906 @default.
- W3165092086 hasConcept C197055811 @default.
- W3165092086 hasConcept C2776257435 @default.
- W3165092086 hasConcept C28826006 @default.
- W3165092086 hasConcept C31258907 @default.
- W3165092086 hasConcept C33923547 @default.
- W3165092086 hasConcept C3770464 @default.
- W3165092086 hasConcept C41008148 @default.
- W3165092086 hasConcept C71134354 @default.
- W3165092086 hasConcept C74127309 @default.
- W3165092086 hasConcept C90119067 @default.
- W3165092086 hasConceptScore W3165092086C105795698 @default.
- W3165092086 hasConceptScore W3165092086C107321475 @default.
- W3165092086 hasConceptScore W3165092086C119857082 @default.
- W3165092086 hasConceptScore W3165092086C126255220 @default.
- W3165092086 hasConceptScore W3165092086C134306372 @default.
- W3165092086 hasConceptScore W3165092086C152877465 @default.
- W3165092086 hasConceptScore W3165092086C177148314 @default.
- W3165092086 hasConceptScore W3165092086C185429906 @default.
- W3165092086 hasConceptScore W3165092086C197055811 @default.
- W3165092086 hasConceptScore W3165092086C2776257435 @default.
- W3165092086 hasConceptScore W3165092086C28826006 @default.
- W3165092086 hasConceptScore W3165092086C31258907 @default.
- W3165092086 hasConceptScore W3165092086C33923547 @default.
- W3165092086 hasConceptScore W3165092086C3770464 @default.
- W3165092086 hasConceptScore W3165092086C41008148 @default.
- W3165092086 hasConceptScore W3165092086C71134354 @default.
- W3165092086 hasConceptScore W3165092086C74127309 @default.
- W3165092086 hasConceptScore W3165092086C90119067 @default.