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FuSSO: Functional Shrinkage and Selection Operator

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Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

Machine Learning · Submitted 10 Nov 2013

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Furthermore, we observe good results on both synthetic and real-world data.

how it is stated · asserted 0.96 read the record →

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arXiv ID1311.2234 (version 2)
Pith IDGXDD3C57, Pith's permanent record of this paper and its review
CitationAarti Singh, Barnabas Poczos, Fang-Cheng Yeh, Jeff Schneider, Junier B. Oliva, Timothy Verstynen, Wen-Yih Tseng. "FuSSO: Functional Shrinkage and Selection Operator." arXiv:1311.2234 (2013). https://pith.science/paper/GXDD3C57
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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,596 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.20 · human participants 0.14 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.11 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. contribution 0.99 0.93 0.40 asserted 1.00
s1 The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. method 0.98 0.59 0.09 asserted 1.00
s2 We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. contribution 0.85 0.96 0.19 asserted 1.00
s3 Furthermore, we observe good results on both synthetic and real-world data. result 1.00 0.95 0.24 asserted 0.96

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.64.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.

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