Stability selection enables robust learning of partial differential equations from limited noisy data | Paper Citation Record

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This paper cites Quantitative modeling in cell biology: what is it good for? Developmental cell, 11(3):279–287.

Stability selection enables robust learning of partial differential equations from limited noisy data Quantitative modeling in cell biology: what is it good for? Developmental cell, 11(3):279–287

Reference 1

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This paper cites Modeling and simulation of biological systems from image data.

Stability selection enables robust learning of partial differential equations from limited noisy data Modeling and simulation of biological systems from image data

Reference 2

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This paper cites Biology by numbers: mathematical modelling in developmental biology.

Stability selection enables robust learning of partial differential equations from limited noisy data Biology by numbers: mathematical modelling in developmental biology

Reference 3

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This paper cites Finite Diﬀerence methods in ﬁnancial engineering: a Partial Di ﬀerential Equation ap- proach.

Stability selection enables robust learning of partial differential equations from limited noisy data Finite Diﬀerence methods in ﬁnancial engineering: a Partial Di ﬀerential Equation ap- proach

Reference 4

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This paper cites Solving the mathematical models of neurosciences and medicine.

Stability selection enables robust learning of partial differential equations from limited noisy data Solving the mathematical models of neurosciences and medicine

Reference 5

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This paper cites 50 years of data science.

Stability selection enables robust learning of partial differential equations from limited noisy data 50 years of data science

Reference 6

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This paper cites Bayesian design of synthetic biological systems.

Stability selection enables robust learning of partial differential equations from limited noisy data Bayesian design of synthetic biological systems

Reference 7

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This paper cites L p-Adaptation: Simultaneous Design Centering and Robustness Estimation of Electronic and Biological Systems.

Stability selection enables robust learning of partial differential equations from limited noisy data L p-Adaptation: Simultaneous Design Centering and Robustness Estimation of Electronic and Biological Systems

Reference 8

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This paper cites Computational systems biology.

Stability selection enables robust learning of partial differential equations from limited noisy data Computational systems biology

Reference 9

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This paper cites Diﬀusion and scaling during early embryonic pattern formation.

Stability selection enables robust learning of partial differential equations from limited noisy data Diﬀusion and scaling during early embryonic pattern formation

Reference 10

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Observation a2d918c7-9d8a-401d-a180-6e9107956f8b · outbound

This paper cites Modeling gene expression with di ﬀerential equations.

Stability selection enables robust learning of partial differential equations from limited noisy data Modeling gene expression with di ﬀerential equations

Reference 11

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This paper cites Mathematical modeling of gene expression: a guide for the perplexed biologist.

Stability selection enables robust learning of partial differential equations from limited noisy data Mathematical modeling of gene expression: a guide for the perplexed biologist

Reference 12

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This paper cites Active gel physics.

Stability selection enables robust learning of partial differential equations from limited noisy data Active gel physics

Reference 13

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This paper cites Attachment of the blastoderm to the vitelline envelope a ﬀects gastrulation of insects.

Stability selection enables robust learning of partial differential equations from limited noisy data Attachment of the blastoderm to the vitelline envelope a ﬀects gastrulation of insects

Reference 14

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This paper cites Identiﬁcation of continuous, spatiotemporal systems.

Stability selection enables robust learning of partial differential equations from limited noisy data Identiﬁcation of continuous, spatiotemporal systems

Reference 15

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This paper cites Friedman.

Stability selection enables robust learning of partial differential equations from limited noisy data Friedman

Reference 16

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This paper cites Fitting partial diﬀerential equations to space-time dynam- ics.

Stability selection enables robust learning of partial differential equations from limited noisy data Fitting partial diﬀerential equations to space-time dynam- ics

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:54.896869Z

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This paper cites an unresolved cited work.

Stability selection enables robust learning of partial differential equations from limited noisy data Unresolved cited work

Reference 18

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This paper cites doi: 10.1080 /01621459.2013.794730.

Stability selection enables robust learning of partial differential equations from limited noisy data doi: 10.1080 /01621459.2013.794730

Reference 19

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Observation ee839ddc-1875-4da4-8ce2-a8bd640cb77a · outbound

This paper cites Machine learning of linear di ﬀerential equa- tions using gaussian processes.

Stability selection enables robust learning of partial differential equations from limited noisy data Machine learning of linear di ﬀerential equa- tions using gaussian processes

Reference 20

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This paper cites Discovering governing equations from data by sparse identiﬁcation of nonlinear dynamical systems.

Stability selection enables robust learning of partial differential equations from limited noisy data Discovering governing equations from data by sparse identiﬁcation of nonlinear dynamical systems

Reference 21

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Observation 31233bc5-8c3c-4ae7-9b49-b02bda382e69 · outbound

This paper cites Data-driven discovery of partial diﬀerential equations.

Stability selection enables robust learning of partial differential equations from limited noisy data Data-driven discovery of partial diﬀerential equations

Reference 22

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This paper cites Learning partial diﬀerential equations via data discovery and sparse optimization.

Stability selection enables robust learning of partial differential equations from limited noisy data Learning partial diﬀerential equations via data discovery and sparse optimization

Reference 23

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This paper cites Robust data-driven discovery of governing physical laws with error bars.Pro- ceedings of the Royal Society A: Mathematical, Physical and Engineering Science , 474(2217):20180305.

Stability selection enables robust learning of partial differential equations from limited noisy data Robust data-driven discovery of governing physical laws with error bars.Pro- ceedings of the Royal Society A: Mathematical, Physical and Engineering Science , 474(2217):20180305

Reference 24

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This paper cites doi: 10.1098/rspa.2018.0305.

Stability selection enables robust learning of partial differential equations from limited noisy data doi: 10.1098/rspa.2018.0305

Reference 25

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This paper cites Model selection for hybrid dynamical systems via sparse regression.

Stability selection enables robust learning of partial differential equations from limited noisy data Model selection for hybrid dynamical systems via sparse regression

Reference 26

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Observation a1fc811c-9204-4d94-b1c8-00416ed4cf6a · outbound

This paper cites PDE-Net: Learning PDEs from Data.

Stability selection enables robust learning of partial differential equations from limited noisy data PDE-Net: Learning PDEs from Data

Reference 27

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This paper cites Hidden physics models: Machine learning of nonlinear partial diﬀerential equations.

Stability selection enables robust learning of partial differential equations from limited noisy data Hidden physics models: Machine learning of nonlinear partial diﬀerential equations

Reference 28

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Observation 612730fd-03f6-40f2-bdd0-534eb5fb1916 · outbound

This paper cites Raissi, P.

Stability selection enables robust learning of partial differential equations from limited noisy data Raissi, P

Reference 29

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This paper cites PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network.

Stability selection enables robust learning of partial differential equations from limited noisy data PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

Reference 30

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This paper cites Image restoration: Wavelet frame shrinkage, nonlinear evolution pdes, and beyond.

Stability selection enables robust learning of partial differential equations from limited noisy data Image restoration: Wavelet frame shrinkage, nonlinear evolution pdes, and beyond

Reference 31

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Observation 4bcb5bc7-08a4-4194-9682-29bca5eec6d2 · outbound

This paper cites Stability selection.

Stability selection enables robust learning of partial differential equations from limited noisy data Stability selection

Reference 32

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This paper cites Variable selection with error control: another look at stability selection.

Stability selection enables robust learning of partial differential equations from limited noisy data Variable selection with error control: another look at stability selection

Reference 33

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This paper cites Regression shrinkage and selection via the lasso.

Stability selection enables robust learning of partial differential equations from limited noisy data Regression shrinkage and selection via the lasso

Reference 34

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Observation bdfc8f5f-810a-4714-87d4-38b1f3e38d53 · outbound

This paper cites Iterative thresholding for sparse approximations.

Stability selection enables robust learning of partial differential equations from limited noisy data Iterative thresholding for sparse approximations

Reference 35

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This paper cites Hard thresholding pursuit: an algorithm for compressive sensing.

Stability selection enables robust learning of partial differential equations from limited noisy data Hard thresholding pursuit: an algorithm for compressive sensing

Reference 36

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Observation aca8be57-ae2f-4aec-8e47-a8949db7231c · outbound

This paper cites Guiding self-organized pattern formation in cell polarity establishment.

Stability selection enables robust learning of partial differential equations from limited noisy data Guiding self-organized pattern formation in cell polarity establishment

Reference 37

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This paper cites Numerical di ﬀerentiation of noisy, nonsmooth data.

Stability selection enables robust learning of partial differential equations from limited noisy data Numerical di ﬀerentiation of noisy, nonsmooth data

Reference 38

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This paper cites Data smoothing and numerical di ﬀerentiation by a regularization method.

Stability selection enables robust learning of partial differential equations from limited noisy data Data smoothing and numerical di ﬀerentiation by a regularization method

Reference 39

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This paper cites Coordinate descent algorithms for lasso penalized regression.

Stability selection enables robust learning of partial differential equations from limited noisy data Coordinate descent algorithms for lasso penalized regression

Reference 40

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This paper cites Regularization paths for generalized linear models via coordinate descent.

Stability selection enables robust learning of partial differential equations from limited noisy data Regularization paths for generalized linear models via coordinate descent

Reference 41

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Observation 63f8a06e-4e24-4978-8e3d-0cf84808ddda · outbound

This paper cites On the douglasrachford splitting method and the proximal point algorithm for maximal monotone operators.

Stability selection enables robust learning of partial differential equations from limited noisy data On the douglasrachford splitting method and the proximal point algorithm for maximal monotone operators

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.244527Z

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This paper cites Proximal splitting methods in signal processing.

Stability selection enables robust learning of partial differential equations from limited noisy data Proximal splitting methods in signal processing

Reference 43

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Observation 564e59ef-10b0-4fc4-b22b-f72c33e242d7 · outbound

This paper cites A Fast Iterative Shrinkage-Thresholding Algorithm.

Stability selection enables robust learning of partial differential equations from limited noisy data A Fast Iterative Shrinkage-Thresholding Algorithm

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.606637Z

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Observation db348143-62c0-4cda-9a84-a6742f948de2 · outbound

This paper cites High-dimensional graphs and variable selection with the lasso.

Stability selection enables robust learning of partial differential equations from limited noisy data High-dimensional graphs and variable selection with the lasso

Reference 45

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Observation efc900e6-bf14-4210-8edb-3bab14ee9490 · outbound

This paper cites On model selection consistency of lasso.

Stability selection enables robust learning of partial differential equations from limited noisy data On model selection consistency of lasso

Reference 46

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Observation 0bdd3cbb-3c7c-4df1-a960-5afcb1739b16 · outbound

This paper cites Variable Selection via Nonconcave Penalized Likelihood and its Oracle Prop- erties.

Stability selection enables robust learning of partial differential equations from limited noisy data Variable Selection via Nonconcave Penalized Likelihood and its Oracle Prop- erties

Reference 47

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Observation af9d3990-2ede-4274-a740-db6dad3b90d9 · outbound

This paper cites Nearly unbiased variable selection under minimax concave penalty , volume 38.

Stability selection enables robust learning of partial differential equations from limited noisy data Nearly unbiased variable selection under minimax concave penalty , volume 38

Reference 48

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Observation be760b27-d5e2-421e-98c7-8b9741123053 · outbound

This paper cites Greed is good: Algorithmic results for sparse approximation.

Stability selection enables robust learning of partial differential equations from limited noisy data Greed is good: Algorithmic results for sparse approximation

Reference 49

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Observation 1391b91a-0e8b-498d-870f-4d2a4f13c82b · outbound

This paper cites CoSaMP: Iterative signal recovery from incomplete and inaccurate samples.

Stability selection enables robust learning of partial differential equations from limited noisy data CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

Reference 50

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Observation 74252e16-334a-49ce-bc70-14daa00d5095 · outbound

This paper cites Subspace pursuit for compressive sensing signal reconstruction.

Stability selection enables robust learning of partial differential equations from limited noisy data Subspace pursuit for compressive sensing signal reconstruction

Reference 51

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Observation 60c9e7e7-3ca2-4963-a8df-398ca4f1d43b · outbound

This paper cites Iterative hard thresholding for compressed sensing.

Stability selection enables robust learning of partial differential equations from limited noisy data Iterative hard thresholding for compressed sensing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:55.024718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ae034091-3cda-44b7-8d44-fec8c1d8309e · outbound

This paper cites Sparse approximation via iterative thresholding.

Stability selection enables robust learning of partial differential equations from limited noisy data Sparse approximation via iterative thresholding

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:55.054695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 395296ce-7b07-47b0-8ce5-c2dba3b47774 · outbound

This paper cites Just relax: Convex programming methods for identifying sparse signals in noise.

Stability selection enables robust learning of partial differential equations from limited noisy data Just relax: Convex programming methods for identifying sparse signals in noise

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:55.061906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 53f0316a-4492-4c37-8e52-edfd445ca2ac · outbound

This paper cites Gradient projection for sparse reconstruc- tion: Application to compressed sensing and other inverse problems.

Stability selection enables robust learning of partial differential equations from limited noisy data Gradient projection for sparse reconstruc- tion: Application to compressed sensing and other inverse problems

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:54.889091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 71de3306-f1f3-4ab1-84ca-65ac75006f58 · outbound

This paper cites A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.

Stability selection enables robust learning of partial differential equations from limited noisy data A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:19:54.900705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a3eb4b3-bd0d-48ce-bcf2-387fd2d61543 · outbound

This paper cites Statist.] 10.1214/aos/1176344136 , 6, 461.

Stability selection enables robust learning of partial differential equations from limited noisy data Statist.] 10.1214/aos/1176344136 , 6, 461

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T19:59:51.968370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 49cedd72-a527-4231-b990-db0edefb5c72 · outbound

This paper cites M ¨uller.

Stability selection enables robust learning of partial differential equations from limited noisy data M ¨uller

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.241310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b1a5bbc-4091-415d-8658-f4aa9b4c34a2 · outbound

This paper cites M ¨uller.

Stability selection enables robust learning of partial differential equations from limited noisy data M ¨uller

Reference 59

Resolution: malformed identifier; arxiv_id, observed 2026-05-24T19:59:52.200047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e05204ec-cfaa-44f3-a781-ca28f503ace3 · outbound

This paper cites Stability.

Stability selection enables robust learning of partial differential equations from limited noisy data Stability

Reference 60

Resolution: malformed identifier; arxiv_id, observed 2026-05-24T19:59:52.192258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 716f7c1b-8756-44b0-b141-acddd6838a84 · outbound

This paper cites Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models.

Stability selection enables robust learning of partial differential equations from limited noisy data Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-24T19:59:52.223772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f1115a7e-9517-4248-81f3-92d639ae5c32 · outbound

This paper cites High-dimensional statistics with a view toward appli- cations in biology.

Stability selection enables robust learning of partial differential equations from limited noisy data High-dimensional statistics with a view toward appli- cations in biology

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.599581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d594918b-9d3b-4cdc-8d4f-4b0e1b7513a8 · outbound

This paper cites Random lasso.

Stability selection enables robust learning of partial differential equations from limited noisy data Random lasso

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.254083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ebb3fb1d-0c04-41d2-bb53-51e0b9f35afd · outbound

This paper cites Navier-Stokes equations: theory and numerical analysis , volume 343.

Stability selection enables robust learning of partial differential equations from limited noisy data Navier-Stokes equations: theory and numerical analysis , volume 343

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.617304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4088d148-29e2-4b4d-b542-c5d4575ee08b · outbound

This paper cites OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers.

Stability selection enables robust learning of partial differential equations from limited noisy data OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.621594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e31f9d9e-7b73-46a0-816b-96a70c14167b · outbound

This paper cites Models of biological pattern formation.

Stability selection enables robust learning of partial differential equations from limited noisy data Models of biological pattern formation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.232085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ef15c280-4f38-4b41-b559-9e9303a71397 · outbound

This paper cites A living mesoscopic cellular automaton made of skin scales.

Stability selection enables robust learning of partial differential equations from limited noisy data A living mesoscopic cellular automaton made of skin scales

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.594850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 559c8560-94c3-42c6-99a9-bc6a5b90b0e9 · outbound

This paper cites Sharp thresholds for high-dimensional and noisy sparsity recovery using l1- con- strained quadratic programming (lasso).

Stability selection enables robust learning of partial differential equations from limited noisy data Sharp thresholds for high-dimensional and noisy sparsity recovery using l1- con- strained quadratic programming (lasso)

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.626197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7bb96752-72ca-449a-ba64-d4dff008804c · outbound

This paper cites Asymmetrically distributed PAR-3 protein contributes to cell polarity and spindle alignment in early C.

Stability selection enables robust learning of partial differential equations from limited noisy data Asymmetrically distributed PAR-3 protein contributes to cell polarity and spindle alignment in early C

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.602696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 274c31e8-e0dc-43bc-a32f-17d890a40a13 · outbound

This paper cites Polarization of PAR proteins by advective triggering of a pattern- forming system.

Stability selection enables robust learning of partial differential equations from limited noisy data Polarization of PAR proteins by advective triggering of a pattern- forming system

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.641886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9e8a7e2b-9b6a-4d49-9be7-3fc7a4cbf68d · outbound

This paper cites Hierarchical grouping to optimize an objective function.

Stability selection enables robust learning of partial differential equations from limited noisy data Hierarchical grouping to optimize an objective function

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.630112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f72db85c-1c9c-46b3-8644-748bca7b35f4 · outbound

This paper cites Data-driven discovery of gov- erning physical laws and their parametric dependencies in engineering, physics and biology.

Stability selection enables robust learning of partial differential equations from limited noisy data Data-driven discovery of gov- erning physical laws and their parametric dependencies in engineering, physics and biology

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.221009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5ea75b14-23ca-4f6e-ad1d-687fcfe38155 · outbound

This paper cites The optimal hard threshold for singular values is 4 / 3.

Stability selection enables robust learning of partial differential equations from limited noisy data The optimal hard threshold for singular values is 4 / 3

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.217595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 094be6bf-3fb8-4ac5-b5fa-87bf099b7628 · outbound

This paper cites Truncated singular value decomposition solutions to discrete ill-posed problems with ill-determined numerical rank.

Stability selection enables robust learning of partial differential equations from limited noisy data Truncated singular value decomposition solutions to discrete ill-posed problems with ill-determined numerical rank

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:16:22.224526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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