Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators | Paper Citation Record

Observation c469c302-03d7-46be-be7f-d9a1ec8d12b9 · outbound

This paper cites Predicting with proxies: Transfer learning in high dimension.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Predicting with proxies: Transfer learning in high dimension

Reference 1

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This paper cites Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality

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This paper cites Near-optimal linear regression under distribution shift.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Near-optimal linear regression under distribution shift

Reference 3

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This paper cites Transfer learning for nonparametric classification.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Transfer learning for nonparametric classification

Reference 4

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This paper cites A class of geometric structures in transfer learning: Minimax bounds and optimality.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A class of geometric structures in transfer learning: Minimax bounds and optimality

Reference 5

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This paper cites Searching for robust associations with a multi-environment knockoff filter.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Searching for robust associations with a multi-environment knockoff filter

Reference 6

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This paper cites Individual data protected integrative regression analysis of high-dimensional heterogeneous data.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Individual data protected integrative regression analysis of high-dimensional heterogeneous data

Reference 7

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This paper cites Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

Reference 8

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This paper cites Adaptive and robust multi-task learning.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Adaptive and robust multi-task learning

Reference 9

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This paper cites Targeting underrepresented populations in precision medicine: A federated transfer learning approach.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Targeting underrepresented populations in precision medicine: A federated transfer learning approach

Reference 10

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Observation 5694e879-b701-4bb8-9972-35e616f9b1d6 · outbound

This paper cites Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure

Reference 11

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Observation 70d7b6fb-9a44-409a-b59d-b4c9103fc0a4 · outbound

This paper cites Statistical challenges of high-dimensional data, 2009.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Statistical challenges of high-dimensional data, 2009

Reference 12

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Observation 4c9c865c-a10a-45a8-9768-f9bfbb3468ef · outbound

This paper cites Message-passing algorithms for compressed sensing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Message-passing algorithms for compressed sensing

Reference 13

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Observation 4c21a413-8950-4d74-9ec4-9a220920b09f · outbound

This paper cites Optimal m-estimation in high-dimensional regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Optimal m-estimation in high-dimensional regression

Reference 14

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Observation 30edaa44-28a2-41c2-abf4-0895449a938c · outbound

This paper cites Precise error analysis of regularized m-estimators in high dimensions.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Precise error analysis of regularized m-estimators in high dimensions

Reference 15

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Observation b0b71b65-1678-4a7a-b87e-f197bb0e54fa · outbound

This paper cites The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square

Reference 16

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Observation 3670d51d-c80d-4dce-97a8-d0ac62fa1ef6 · outbound

This paper cites A modern maximum-likelihood theory for high-dimensional logistic regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A modern maximum-likelihood theory for high-dimensional logistic regression

Reference 17

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Observation f64e1d64-6c0c-4f90-ad68-4ed060e12540 · outbound

This paper cites The impact of regularization on high-dimensional logistic regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The impact of regularization on high-dimensional logistic regression

Reference 18

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Observation e78fa727-3c9c-4aeb-b0e8-f830f34e4e81 · outbound

This paper cites The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression

Reference 19

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Observation 06567519-96f6-4586-b052-85fa8b6e6e05 · outbound

This paper cites Optimal errors and phase transitions in high-dimensional generalized linear models.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Optimal errors and phase transitions in high-dimensional generalized linear models

Reference 20

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This paper cites Which bridge estimator is the best for variable selection? The Annals of Statistics , 48(5):2791 – 2823, 2020.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Which bridge estimator is the best for variable selection? The Annals of Statistics , 48(5):2791 – 2823, 2020

Reference 21

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Observation 10530f05-5128-456d-9638-f6e6dfcb9e4a · outbound

This paper cites Approximate message passing with spectral initialization for generalized linear models.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Approximate message passing with spectral initialization for generalized linear models

Reference 22

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Observation b89d369b-5959-4901-82eb-60b54a58b1b0 · outbound

This paper cites Phase transitions in transfer learning for high-dimensional perceptrons.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Phase transitions in transfer learning for high-dimensional perceptrons

Reference 23

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Observation 4453c70a-a4ac-4c8b-b193-88fe0924f29d · outbound

This paper cites The asymptotic distribution of the mle in high-dimensional logistic models: Arbitrary covariance.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The asymptotic distribution of the mle in high-dimensional logistic models: Arbitrary covariance

Reference 24

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This paper cites A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond

Reference 25

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This paper cites Surprises in high-dimensional ridgeless least squares interpolation.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Surprises in high-dimensional ridgeless least squares interpolation

Reference 26

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This paper cites A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers

Reference 27

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This paper cites The lasso with general gaussian designs with applications to hypothesis testing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The lasso with general gaussian designs with applications to hypothesis testing

Reference 28

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This paper cites HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators

Reference 29

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This paper cites Roti-gcv: Generalized cross-validation for right-rotationally invariant data.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Roti-gcv: Generalized cross-validation for right-rotationally invariant data

Reference 30

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This paper cites Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

Reference 31

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Observation 33158c93-f62f-4c5c-a21b-f7aa98c43f9d · outbound

This paper cites The lasso risk for gaussian matrices.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The lasso risk for gaussian matrices

Reference 32

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This paper cites High dimensional robust m-estimation: Asymptotic variance via approximate message passing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators High dimensional robust m-estimation: Asymptotic variance via approximate message passing

Reference 33

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Observation dc78e5ea-eadb-4e67-8f6e-46d92d840d1d · outbound

This paper cites Statistical physics of inference: Thresholds and algorithms.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Statistical physics of inference: Thresholds and algorithms

Reference 34

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This paper cites A unifying tutorial on approximate message passing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A unifying tutorial on approximate message passing

Reference 35

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This paper cites A friendly tutorial on mean-field spin glass techniques for non-physicists.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A friendly tutorial on mean-field spin glass techniques for non-physicists

Reference 36

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This paper cites An iterative construction of solutions of the tap equations for the sherrington–kirkpatrick model.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators An iterative construction of solutions of the tap equations for the sherrington–kirkpatrick model

Reference 37

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This paper cites The dynamics of message passing on dense graphs, with applications to compressed sensing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The dynamics of message passing on dense graphs, with applications to compressed sensing

Reference 38

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This paper cites Generalized approximate message passing for estimation with random linear mixing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Generalized approximate message passing for estimation with random linear mixing

Reference 39

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This paper cites State evolution for general approximate message passing algorithms, with applications to spatial coupling.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators State evolution for general approximate message passing algorithms, with applications to spatial coupling

Reference 40

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Observation b926f5d1-8f8a-40ea-874e-85dd65f957d2 · outbound

This paper cites State evolution for approximate message passing with non-separable functions.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators State evolution for approximate message passing with non-separable functions

Reference 41

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Observation 02fba40d-f697-48ed-96d8-c112d55140d8 · outbound

This paper cites Graph-based approximate message passing iterations.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Graph-based approximate message passing iterations

Reference 42

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Observation fa262f9c-4606-4353-b5f5-33c84aace974 · outbound

This paper cites Solution of’solvable model of a spin glass’.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Solution of’solvable model of a spin glass’

Reference 43

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Observation a7a90669-1953-4bd4-b4cb-4770855b3d41 · outbound

This paper cites Graphical models concepts in compressed sensing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Graphical models concepts in compressed sensing

Reference 44

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Observation 2079a54e-e16d-4174-8a06-f97ea4b0303c · outbound

This paper cites Estimating lasso risk and noise level.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Estimating lasso risk and noise level

Reference 45

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Observation cb40b077-5982-48b8-b345-eb26a5c49280 · outbound

This paper cites Non-negative principal component analysis: Message passing algorithms and sharp asymptotics.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Non-negative principal component analysis: Message passing algorithms and sharp asymptotics

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:29.648035Z

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Observation b1e6b82b-3c70-4f49-9e11-237b29167da3 · outbound

This paper cites Asymptotics of map inference in deep networks.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Asymptotics of map inference in deep networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:29.457241Z

Source-reported events for the cited work

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Observation 878c5ebc-4f7a-4d4f-88fa-c6551c97c881 · outbound

This paper cites Approximate message-passing decoder and capacity achieving sparse superposition codes.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Approximate message-passing decoder and capacity achieving sparse superposition codes

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:29.255421Z

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Observation 805bda2b-24a5-4929-8508-24e391589bd6 · outbound

This paper cites Vector approximate message passing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Vector approximate message passing

Reference 49

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Observation 3a0042e1-d778-4106-af15-3be0335f46a4 · outbound

This paper cites Orthogonal amp.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Orthogonal amp

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:28.772781Z

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Observation 100d5c33-1588-428c-a618-8dd165970164 · outbound

This paper cites Approximate message passing algorithms for rotationally invariant matrices.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Approximate message passing algorithms for rotationally invariant matrices

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:28.545205Z

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Observation 96a1a5e9-7388-4483-a37e-f79bfd259656 · outbound

This paper cites Finite sample analysis of approximate message passing algorithms.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Finite sample analysis of approximate message passing algorithms

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:28.324697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1dc2251c-7e3d-401f-84c7-df86569ef0cc · outbound

This paper cites A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:14.416167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8152b914-8254-4f0e-98d6-89cf33fe6799 · outbound

This paper cites Transfusion: Covariate-shift robust transfer learning for high-dimensional regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Transfusion: Covariate-shift robust transfer learning for high-dimensional regression

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:28.061552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b0e0a5b3-4f4c-447a-8292-87ca9955d461 · outbound

This paper cites Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:14.520230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd9effc-8d03-422d-a9f2-96e42a000513 · outbound

This paper cites Algorithmic analysis and statistical estimation of slope via approximate message passing.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Algorithmic analysis and statistical estimation of slope via approximate message passing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:27.867825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fc098763-3e68-462e-ae2e-ae39cc2c8ccc · outbound

This paper cites Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:14.678333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7b04d2e-bb88-4231-8971-e29fe7227efd · outbound

This paper cites Chi-square and normal inference in high-dimensional multi-task regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Chi-square and normal inference in high-dimensional multi-task regression

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:12:19.047640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 94f00e75-1c2d-4df8-b647-0db267f344ae · outbound

This paper cites Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:14.853849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e266834e-4352-4d5f-920c-6e940ee66386 · outbound

This paper cites Covariate Shift in High-Dimensional Random Feature Regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Covariate Shift in High-Dimensional Random Feature Regression

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:12:18.856399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d94fc74e-9845-4be3-9040-8b8fd4660086 · outbound

This paper cites Generalization error of min-norm interpolators in transfer learning.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Generalization error of min-norm interpolators in transfer learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:15.013115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 103bc2b9-18e7-44b9-9845-dc64c708ff5a · outbound

This paper cites Optimal Ridge Regularization for Out-of-Distribution Prediction.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Optimal Ridge Regularization for Out-of-Distribution Prediction

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:15.130606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74109a72-65b3-46b2-9392-95571b25d365 · outbound

This paper cites Minimum-Norm Interpolation Under Covariate Shift.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Minimum-Norm Interpolation Under Covariate Shift

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:12:18.592010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b136f8a-e059-4205-b499-48cfbb2c7e51 · outbound

This paper cites Predictive Inference in Multi-environment Scenarios.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Predictive Inference in Multi-environment Scenarios

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:15.273087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3184af88-2bfb-4706-ae15-2213b0ea4fb7 · outbound

This paper cites The adaptive lasso and its oracle properties.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators The adaptive lasso and its oracle properties

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:27.642599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57a6fa3f-52d3-405e-b225-d3311354739d · outbound

This paper cites Universality in polytope phase transitions and message passing algorithms.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality in polytope phase transitions and message passing algorithms

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:15.399770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a113f0e-5a89-4e7b-9119-2ba59376f59c · outbound

This paper cites Universality of approximate message passing algorithms.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality of approximate message passing algorithms

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:27.299286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 341814e3-fec2-47d7-9c16-4f09f6445dd9 · outbound

This paper cites Universality laws for high-dimensional learning with random features.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality laws for high-dimensional learning with random features

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:27.102994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88c9dba1-e463-4394-9587-1a1b9950c323 · outbound

This paper cites Universality of empirical risk minimization.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality of empirical risk minimization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:26.793554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d0493947-95e4-4774-8301-1728701ad5a2 · outbound

This paper cites Lu, and Subhabrata Sen.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Lu, and Subhabrata Sen

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:26.612385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d602991-b5b6-4b07-b9ca-ac7377d3796e · outbound

This paper cites Spectrum-aware debiasing: A modern inference framework with applications to principal components regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Spectrum-aware debiasing: A modern inference framework with applications to principal components regression

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:15.731928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 278f4c70-5e48-4dae-a1ad-8f197f72f247 · outbound

This paper cites Universality of approximate message passing algorithms and tensor networks.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality of approximate message passing algorithms and tensor networks

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:26.392822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 976cd99b-fa74-409d-9cc8-cf633a436803 · outbound

This paper cites Universality in block dependent linear models with applications to nonlinear regression.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality in block dependent linear models with applications to nonlinear regression

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:26.169173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 36f9de21-2293-4819-abac-93cc1b00afcb · outbound

This paper cites Universality in transfer learning for linear models.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Universality in transfer learning for linear models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:25.960461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 39688297-c07b-4652-be82-077e8b8c5d16 · outbound

This paper cites Adaptive transfer learning.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Adaptive transfer learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:25.756842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ee35265-3feb-42af-a181-25eab4188158 · outbound

This paper cites A no-free-lunch theorem for multitask learning.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A no-free-lunch theorem for multitask learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:25.487254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c3ac8e2c-ce65-49f7-a072-be69cb6cabfd · outbound

This paper cites Estimation and inference for high-dimensional generalized linear models with knowledge transfer.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Estimation and inference for high-dimensional generalized linear models with knowledge transfer

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:25.225048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ae33f2c-14b6-4561-9869-e09c4fc28254 · outbound

This paper cites Transfer learning under high-dimensional generalized linear models.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Transfer learning under high-dimensional generalized linear models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:25.019420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f634dc6-06b5-4258-898a-70cdb4f7e46e · outbound

This paper cites A linear adjustment-based approach to posterior drift in transfer learning.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A linear adjustment-based approach to posterior drift in transfer learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:24.775696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6df784d6-828b-4eb9-a6fb-5d156baf0d99 · outbound

This paper cites Inequalities for the trace of matrix product.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Inequalities for the trace of matrix product

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:24.563009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95245660-e36d-49af-b567-790c8cf2b7b0 · outbound

This paper cites Lasso risk and phase transition under dependence.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Lasso risk and phase transition under dependence

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:24.321085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3795cbc9-a8e5-460f-9a83-0e4385264ae9 · outbound

This paper cites Limit of the smallest eigenvalue of a large dimensional sample covariance matrix.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Limit of the smallest eigenvalue of a large dimensional sample covariance matrix

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:24.130677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b89eb138-276c-4c88-ac71-d7211be005c0 · outbound

This paper cites Introduction to the non-asymptotic analysis of random matrices.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Introduction to the non-asymptotic analysis of random matrices

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:16.628724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb4ff19f-ba46-4b2e-868e-cdd6532a538b · outbound

This paper cites Lemma F.5) applies just like in the proof of Lemma 3.2, Bayati and Montanari [32].

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Lemma F.5) applies just like in the proof of Lemma 3.2, Bayati and Montanari [32]

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:23.905699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f073379b-f110-49ab-93d3-c3f2bfdcb7d8 · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:23.682714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cdc153ff-7c1f-4639-b822-6a276e5b0329 · outbound

This paper cites Simplifying it with Assumption 3 and 4, Σ1 (V,e) = E[W 2 e ] + κe limp 1 p E[∥η − βe∥2 Σe] = (τ ∗ e )2.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Simplifying it with Assumption 3 and 4, Σ1 (V,e) = E[W 2 e ] + κe limp 1 p E[∥η − βe∥2 Σe] = (τ ∗ e )2

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:23.483273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d18802a1-006e-463c-9ac8-13a7c1ccb5bd · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:23.286739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 707a456d-0343-4386-9bec-a4f8855a1af9 · outbound

This paper cites For the diagnoal elements of Σ 2 (V,e) we have simplified it in the same way as Σ 1 (V,e).

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators For the diagnoal elements of Σ 2 (V,e) we have simplified it in the same way as Σ 1 (V,e)

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:23.154748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc954685-3fe9-4f0b-a54b-37c7e4486126 · outbound

This paper cites We plan to show ρt,t+1 e converges exponentially fast to 1 for each e ∈ [E] with an argument of fixed point iteration.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators We plan to show ρt,t+1 e converges exponentially fast to 1 for each e ∈ [E] with an argument of fixed point iteration

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:22.966802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d18a84a-d77d-4d1a-926f-370ef76814c6 · outbound

This paper cites We use the first line of Equation (C-23) to bound 1 p ∥∆η(2)∥2.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators We use the first line of Equation (C-23) to bound 1 p ∥∆η(2)∥2

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:22.736310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 020b8edb-2137-455c-985f-fb34c2164be9 · outbound

This paper cites Since ∆ η(2) = ∆ηt − ∆η(1), we know 1 p ∥diag(⃗λSc)(∆η(2))Sc∥1 − 1 p [diag(⃗λSc)st Sc]⊤(∆η(2))Sc ≤ ϵ2 · c2 2c4 3 + 4 √ 2ϵc2c3, where we have used the fact that M > 1 from (i).

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Since ∆ η(2) = ∆ηt − ∆η(1), we know 1 p ∥diag(⃗λSc)(∆η(2))Sc∥1 − 1 p [diag(⃗λSc)st Sc]⊤(∆η(2))Sc ≤ ϵ2 · c2 2c4 3 + 4 √ 2ϵc2c3, where we have used the fact that M > 1 from (i)

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:22.556209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43bb8588-cec5-4379-a751-152f83b9ac73 · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:22.328569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ff7836f-6587-49b4-87e0-10f3f9794559 · outbound

This paper cites Simplifying it with Assumption 5 and 6, Σ1 (ind,V,e) = E[W 2 e ] + κe limp 1 p E[∥ηe − βe∥2 Σ(ind,e) ] = (τ ∗ ind,e)2.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Simplifying it with Assumption 5 and 6, Σ1 (ind,V,e) = E[W 2 e ] + κe limp 1 p E[∥ηe − βe∥2 Σ(ind,e) ] = (τ ∗ ind,e)2

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:22.077655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59da4bb3-82b9-4fff-bb99-b20f73f6b074 · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:21.834179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d580dfb-658e-4cee-8797-da3528fed958 · outbound

This paper cites For the diagnoal elements of Σ 2 (ind,V,e) we have simplified it in the same way as Σ 1 (ind,V,e).

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators For the diagnoal elements of Σ 2 (ind,V,e) we have simplified it in the same way as Σ 1 (ind,V,e)

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:21.635275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 165f5a2f-ba25-407c-8d8e-e8a193863c42 · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:21.509991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e895b8bb-76bf-44e3-b2d0-a2008df23d3e · outbound

This paper cites We are left to verify that the state evolution is well-defined, and satisfies the marginal properties 68 in Lemma E.2.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators We are left to verify that the state evolution is well-defined, and satisfies the marginal properties 68 in Lemma E.2

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:21.282155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 35ae32ca-2eb2-4877-9453-7c4fa02cf335 · outbound

This paper cites 71 By the law of iterated expectations, HII(1) ≥ HII(0).

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators 71 By the law of iterated expectations, HII(1) ≥ HII(0)

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:21.086669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 873b849b-b331-47c8-a7cd-05b3e110dd9e · outbound

This paper cites Then sII ∈ ∇µII(ξ; η), or in the case of the joint estimator, sII/λII ∈ ∂∥ξ − η∥1.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Then sII ∈ ∇µII(ξ; η), or in the case of the joint estimator, sII/λII ∈ ∂∥ξ − η∥1

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:20.851399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a56e30e0-3d36-4483-a9b5-c392964d65d9 · outbound

This paper cites an unresolved cited work.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:20.654646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers