Differentially Private Learning Beyond the Classical Dimensionality Regime | Paper Citation Record

Observation 230810b5-52af-4990-9f69-ffbd4ad48760 · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Differentially Private Learning Beyond the Classical Dimensionality Regime Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 1

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This paper cites Easy differentially private linear regression.

Differentially Private Learning Beyond the Classical Dimensionality Regime Easy differentially private linear regression

Reference 2

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Observation 190a7a44-2e9e-4db0-a976-dd5dd6fd240f · outbound

This paper cites Private mean estimation with person-level differential privacy.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private mean estimation with person-level differential privacy

Reference 3

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This paper cites Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang.

Differentially Private Learning Beyond the Classical Dimensionality Regime Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang

Reference 4

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Differentially Private Learning Beyond the Classical Dimensionality Regime Private and polynomial time algorithms for learning gaussians and beyond

Reference 5

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This paper cites McCoy, and Joel A.

Differentially Private Learning Beyond the Classical Dimensionality Regime McCoy, and Joel A

Reference 6

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This paper cites Universality in learning from linear measurements.

Differentially Private Learning Beyond the Classical Dimensionality Regime Universality in learning from linear measurements

Reference 7

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This paper cites Private gradient descent for linear regression: Tighter error bounds and instance-specific uncertainty estimation.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private gradient descent for linear regression: Tighter error bounds and instance-specific uncertainty estimation

Reference 8

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This paper cites Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C.

Differentially Private Learning Beyond the Classical Dimensionality Regime Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C

Reference 9

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Observation 472a5c79-0da0-45c1-bf9b-55bbeac084f7 · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

Differentially Private Learning Beyond the Classical Dimensionality Regime Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 10

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Observation 4214be21-3877-4c9b-860b-6ebacb4f5abe · outbound

This paper cites Fast, sample-efficient, affine-invariant private mean and covariance estimation for subgaussian distributions.

Differentially Private Learning Beyond the Classical Dimensionality Regime Fast, sample-efficient, affine-invariant private mean and covariance estimation for subgaussian distributions

Reference 11

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Observation 0dd0715b-343f-4b66-9c13-91efb2ed5604 · outbound

This paper cites Two models of double descent for weak features.

Differentially Private Learning Beyond the Classical Dimensionality Regime Two models of double descent for weak features

Reference 12

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Observation 547d9deb-7bdd-451d-ada6-e6351f2d2641 · outbound

This paper cites A leave-one-out approach to approximate message passing, 2023.

Differentially Private Learning Beyond the Classical Dimensionality Regime A leave-one-out approach to approximate message passing, 2023

Reference 13

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Observation 0bb74595-78aa-417e-b9f3-88fb79b3107b · outbound

This paper cites Private hypothesis selection.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private hypothesis selection

Reference 14

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Observation dbf45254-e767-41e9-82f0-5cf7931faffc · outbound

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

Differentially Private Learning Beyond the Classical Dimensionality Regime Universality in polytope phase transitions and message passing algorithms

Reference 15

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Observation fbe5d9b4-0873-400a-8134-defd62799e46 · outbound

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

Differentially Private Learning Beyond the Classical Dimensionality Regime The dynamics of message passing on dense graphs, with applications to compressed sensing

Reference 16

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Observation 77c38b4c-7f0e-4407-ad67-39ff7030c732 · outbound

This paper cites The lasso risk for gaussian matrices.

Differentially Private Learning Beyond the Classical Dimensionality Regime The lasso risk for gaussian matrices

Reference 17

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Observation e243763f-01fa-43b2-90be-20a10603bf50 · outbound

This paper cites Concentrated differential privacy: Simplifications, extensions, and lower bounds.

Differentially Private Learning Beyond the Classical Dimensionality Regime Concentrated differential privacy: Simplifications, extensions, and lower bounds

Reference 18

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This paper cites Smith, and Abhradeep Thakurta.

Differentially Private Learning Beyond the Classical Dimensionality Regime Smith, and Abhradeep Thakurta

Reference 19

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Observation 8311e928-3d8c-490c-89bc-006dbe524685 · outbound

This paper cites Convex optimization: Algorithms and complexity.

Differentially Private Learning Beyond the Classical Dimensionality Regime Convex optimization: Algorithms and complexity

Reference 20

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Observation cfb96941-4c0f-4b30-a2d6-b17c7a93a2cc · outbound

This paper cites Improving the gaussian mechanism for differential privacy: A nalytical calibration and optimal denoising.

Differentially Private Learning Beyond the Classical Dimensionality Regime Improving the gaussian mechanism for differential privacy: A nalytical calibration and optimal denoising

Reference 21

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This paper cites Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman.

Differentially Private Learning Beyond the Classical Dimensionality Regime Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman

Reference 22

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 23

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This paper cites The estimation error of general first order methods.

Differentially Private Learning Beyond the Classical Dimensionality Regime The estimation error of general first order methods

Reference 24

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This paper cites The Lasso with general Gaussian designs with applications to hypothesis testing.

Differentially Private Learning Beyond the Classical Dimensionality Regime The Lasso with general Gaussian designs with applications to hypothesis testing

Reference 25

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Observation a5f95310-0233-409b-b353-912969f18fd8 · outbound

This paper cites Cand \`e s and Pragya Sur.

Differentially Private Learning Beyond the Classical Dimensionality Regime Cand \`e s and Pragya Sur

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:22.376812Z

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This paper cites Tony Cai, Yichen Wang, and Linjun Zhang.

Differentially Private Learning Beyond the Classical Dimensionality Regime Tony Cai, Yichen Wang, and Linjun Zhang

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:22.362113Z

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Observation a17b87d0-f0c2-415c-bdad-ae3ead4921a4 · outbound

This paper cites Tony Cai, Yichen Wang, and Linjun Zhang.

Differentially Private Learning Beyond the Classical Dimensionality Regime Tony Cai, Yichen Wang, and Linjun Zhang

Reference 28

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Observation 70d066a8-781f-46e1-8ebb-239da5da5144 · outbound

This paper cites Our data, ourselves: Privacy via distributed noise generation.

Differentially Private Learning Beyond the Classical Dimensionality Regime Our data, ourselves: Privacy via distributed noise generation

Reference 29

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Observation ea5da6b8-5afb-461b-8175-fa563c9a056d · outbound

This paper cites A model of double descent for high-dimensional binary linear classification.

Differentially Private Learning Beyond the Classical Dimensionality Regime A model of double descent for high-dimensional binary linear classification

Reference 30

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Observation 59658f55-0eae-4c1b-82bd-82a86c3417f3 · outbound

This paper cites Differential privacy and robust statistics.

Differentially Private Learning Beyond the Classical Dimensionality Regime Differential privacy and robust statistics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:22.293554Z

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 32

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This paper cites Lu, and Subhabrata Sen.

Differentially Private Learning Beyond the Classical Dimensionality Regime Lu, and Subhabrata Sen

Reference 33

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This paper cites Message-passing algorithms for compressed sensing.

Differentially Private Learning Beyond the Classical Dimensionality Regime Message-passing algorithms for compressed sensing

Reference 34

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This paper cites The noise-sensitivity phase transition in compressed sensing.

Differentially Private Learning Beyond the Classical Dimensionality Regime The noise-sensitivity phase transition in compressed sensing

Reference 35

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 36

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This paper cites The algorithmic foundations of differential privacy.

Differentially Private Learning Beyond the Classical Dimensionality Regime The algorithmic foundations of differential privacy

Reference 37

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Differentially Private Learning Beyond the Classical Dimensionality Regime Rothblum

Reference 38

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 39

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This paper cites Asymptotic behavior of unregularized and ridge-regularized high-dimensional robust regression estimators: rigorous results, 2013.

Differentially Private Learning Beyond the Classical Dimensionality Regime Asymptotic behavior of unregularized and ridge-regularized high-dimensional robust regression estimators: rigorous results, 2013

Reference 40

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This paper cites On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators.

Differentially Private Learning Beyond the Classical Dimensionality Regime On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:22.150682Z

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Observation 0c06a2d7-8dac-4345-aed6-b5d19e65390b · outbound

This paper cites On robust regression with high-dimensional predictors.

Differentially Private Learning Beyond the Classical Dimensionality Regime On robust regression with high-dimensional predictors

Reference 42

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Observation 2392415d-90c6-480e-a5c7-cbe94c61483a · outbound

This paper cites Approximate Message Passing algorithms for rotationally invariant matrices.

Differentially Private Learning Beyond the Classical Dimensionality Regime Approximate Message Passing algorithms for rotationally invariant matrices

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:22.121990Z

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 44

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Observation 89d03239-37c7-408e-aed8-9de5fd43f24c · outbound

This paper cites Locally private hypothesis selection.

Differentially Private Learning Beyond the Classical Dimensionality Regime Locally private hypothesis selection

Reference 45

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This paper cites Some inequalities for gaussian processes and applications.

Differentially Private Learning Beyond the Classical Dimensionality Regime Some inequalities for gaussian processes and applications

Reference 46

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Observation 762972cf-8008-4b99-bbd6-7d81f0447342 · outbound

This paper cites Rigorous dynamical mean-field theory for stochastic gradient descent methods.

Differentially Private Learning Beyond the Classical Dimensionality Regime Rigorous dynamical mean-field theory for stochastic gradient descent methods

Reference 47

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Observation 652d459c-fdc8-4bf9-9229-4fbba8cdfb95 · outbound

This paper cites Entrywise dynamics and universality of general first order methods, 2024.

Differentially Private Learning Beyond the Classical Dimensionality Regime Entrywise dynamics and universality of general first order methods, 2024

Reference 48

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This paper cites Hopkins, Gautam Kamath, and Mahbod Majid.

Differentially Private Learning Beyond the Classical Dimensionality Regime Hopkins, Gautam Kamath, and Mahbod Majid

Reference 49

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This paper cites Hopkins, Gautam Kamath, Mahbod Majid, and Shyam Narayanan.

Differentially Private Learning Beyond the Classical Dimensionality Regime Hopkins, Gautam Kamath, Mahbod Majid, and Shyam Narayanan

Reference 50

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Observation d63ac5e7-7155-4171-b5cd-141691b05f64 · outbound

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:22.007256Z

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Observation 7b328055-f150-45f2-a9ae-3f8c29ebe43f · outbound

This paper cites Universality of regularized regression estimators in high dimensions.

Differentially Private Learning Beyond the Classical Dimensionality Regime Universality of regularized regression estimators in high dimensions

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.993842Z

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Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 53

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Observation 65f070e6-4af6-4cb6-ae1f-3705b765a5ac · outbound

This paper cites Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang.

Differentially Private Learning Beyond the Classical Dimensionality Regime Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.962324Z

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Observation 9153c93b-46d1-41af-b0b1-bd23393ade90 · outbound

This paper cites (near) dimension independent risk bounds for differentially private learning.

Differentially Private Learning Beyond the Classical Dimensionality Regime (near) dimension independent risk bounds for differentially private learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.948328Z

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Observation c8e9d87b-1ea4-4a6f-b2ff-37d48e2c2dd4 · outbound

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Differentially Private Learning Beyond the Classical Dimensionality Regime Berrett, and Yi Yu

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.933417Z

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Observation 417f4aa2-8125-43f9-9876-852305cae789 · outbound

This paper cites A pretty fast algorithm for adaptive private mean estimation.

Differentially Private Learning Beyond the Classical Dimensionality Regime A pretty fast algorithm for adaptive private mean estimation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.920821Z

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Observation 1a8fb8aa-3799-4034-90e9-376f263726ec · outbound

This paper cites Privately learning high-dimensional distributions.

Differentially Private Learning Beyond the Classical Dimensionality Regime Privately learning high-dimensional distributions

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.908969Z

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Observation 25866872-8f73-47a1-9214-368f8768f559 · outbound

This paper cites Applications of the lindeberg principle in communications and statistical learning.

Differentially Private Learning Beyond the Classical Dimensionality Regime Applications of the lindeberg principle in communications and statistical learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.896681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1cd9f534-d337-4369-98c6-df644fd11b6a · outbound

This paper cites A private and computationally-efficient estimator for unbounded gaussians.

Differentially Private Learning Beyond the Classical Dimensionality Regime A private and computationally-efficient estimator for unbounded gaussians

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.883543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c1dd024b-433c-45cc-bec3-e2aef0453fe8 · outbound

This paper cites Private robust estimation by stabilizing convex relaxations.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private robust estimation by stabilizing convex relaxations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.871022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 73c70be1-3940-4f8c-af73-370645e6c159 · outbound

This paper cites Private convex empirical risk minimization and high-dimensional regression.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private convex empirical risk minimization and high-dimensional regression

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.857907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f99844ba-9544-483b-8d6c-556b2b3f25d8 · outbound

This paper cites Private mean estimation of heavy-tailed distributions.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private mean estimation of heavy-tailed distributions

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.842925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6a63a750-950d-47da-b709-8b8fc9530086 · outbound

This paper cites Learning curves of generic features maps for realistic datasets with a teacher-student model.

Differentially Private Learning Beyond the Classical Dimensionality Regime Learning curves of generic features maps for realistic datasets with a teacher-student model

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.824994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 51e2817e-d4fe-4db5-b363-10dc27387674 · outbound

This paper cites Kakade, and Sewoong Oh.

Differentially Private Learning Beyond the Classical Dimensionality Regime Kakade, and Sewoong Oh

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.810305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 90113d05-3e99-4cf8-9099-3da31efe2374 · outbound

This paper cites Differential privacy and robust statistics in high dimensions.

Differentially Private Learning Beyond the Classical Dimensionality Regime Differential privacy and robust statistics in high dimensions

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.797560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cff4f36e-0abd-4e5f-b7bf-55dd1fb9a54b · outbound

This paper cites A precise high-dimensional asymptotic theory for boosting and minimum- _ 1 -norm interpolated classifiers.

Differentially Private Learning Beyond the Classical Dimensionality Regime A precise high-dimensional asymptotic theory for boosting and minimum- _ 1 -norm interpolated classifiers

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.782249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80e71ef2-0451-401c-935b-aefcb9d4dc53 · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:21.769024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a34d211d-8cc2-46cf-b454-277875dd133a · outbound

This paper cites R \' e nyi differential privacy.

Differentially Private Learning Beyond the Classical Dimensionality Regime R \' e nyi differential privacy

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.754529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d83feb6f-a186-467f-85b2-fc3eb7c2eaa5 · outbound

This paper cites Better locally private sparse estimation given multiple samples per user.

Differentially Private Learning Beyond the Classical Dimensionality Regime Better locally private sparse estimation given multiple samples per user

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.742053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 718b4161-17a8-44bf-aaf4-00d584ef9f56 · outbound

This paper cites The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning.

Differentially Private Learning Beyond the Classical Dimensionality Regime The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.728128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 395b53dd-8c42-4a2c-a262-73638c9e5499 · outbound

This paper cites The generalization error of random features regression: Precise asymptotics and the double descent curve.

Differentially Private Learning Beyond the Classical Dimensionality Regime The generalization error of random features regression: Precise asymptotics and the double descent curve

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.714292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bc0ad3f6-5e7f-42c5-a118-f92131a5e09d · outbound

This paper cites Universality of the elastic net error.

Differentially Private Learning Beyond the Classical Dimensionality Regime Universality of the elastic net error

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.700387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2f6fab89-0b9b-47fd-aac2-a1233a99ac62 · outbound

This paper cites The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime, 2023.

Differentially Private Learning Beyond the Classical Dimensionality Regime The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime, 2023

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.684907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 128b2602-5ae8-44b1-970c-dbf6ee6eb123 · outbound

This paper cites Private high-dimensional hypothesis testing.

Differentially Private Learning Beyond the Classical Dimensionality Regime Private high-dimensional hypothesis testing

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.670545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7d78b64d-0a04-4921-891b-672b43f4f0c3 · outbound

This paper cites Oracle efficient private non-convex optimization.

Differentially Private Learning Beyond the Classical Dimensionality Regime Oracle efficient private non-convex optimization

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.655564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7bb0bd58-423e-448f-9b97-8a6225bdbade · outbound

This paper cites Universality laws for randomized dimension reduction, with applications.

Differentially Private Learning Beyond the Classical Dimensionality Regime Universality laws for randomized dimension reduction, with applications

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.640418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7fbdbf5e-ed9e-498b-a518-45185d7f1512 · outbound

This paper cites Pour, Hassan Ashtiani, and Shahab Asoodeh.

Differentially Private Learning Beyond the Classical Dimensionality Regime Pour, Hassan Ashtiani, and Shahab Asoodeh

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.624209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0d422676-81e9-4365-bd60-3a83b1804a3c · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:21.608631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e24d25a7-d776-487d-8ce0-ba1b62cf54b4 · outbound

This paper cites A universal analysis of large-scale regularized least squares solutions.

Differentially Private Learning Beyond the Classical Dimensionality Regime A universal analysis of large-scale regularized least squares solutions

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.593570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 77575fd9-9bf3-45f6-bf95-ee629c532ed2 · outbound

This paper cites Improving the privacy and practicality of objective perturbation for differentially private linear learners.

Differentially Private Learning Beyond the Classical Dimensionality Regime Improving the privacy and practicality of objective perturbation for differentially private linear learners

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.580916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1def3c2c-f7a3-41a9-adae-cb870ccb2947 · outbound

This paper cites Privately publishable per-instance privacy.

Differentially Private Learning Beyond the Classical Dimensionality Regime Privately publishable per-instance privacy

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.566376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b5be6d2c-bdbe-409d-8b81-bcfb1c92568c · outbound

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

Differentially Private Learning Beyond the Classical Dimensionality Regime The impact of regularization on high-dimensional logistic regression

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.550401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d2f627d0-e01e-490e-9896-a17ef03ed3dd · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:21.534440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 85fb481f-d08a-44b6-aea5-f4519694ef8c · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:21.519329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0d6a21f3-9deb-429e-91bf-9996393a90f4 · outbound

This paper cites Differentially private ordinary least squares.

Differentially Private Learning Beyond the Classical Dimensionality Regime Differentially private ordinary least squares

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.503202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3b6dda7e-3d82-4153-86c9-42c96ae7daa1 · outbound

This paper cites Old techniques in differentially private linear regression.

Differentially Private Learning Beyond the Classical Dimensionality Regime Old techniques in differentially private linear regression

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.479370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ada5a87-21dd-4d44-a4ff-6b57c7fdb298 · outbound

This paper cites On general minimax theorems.

Differentially Private Learning Beyond the Classical Dimensionality Regime On general minimax theorems

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.460695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 896035cb-6d09-47b4-a1da-6b5a7acaf453 · outbound

This paper cites The one-sided barrier problem for gaussian noise.

Differentially Private Learning Beyond the Classical Dimensionality Regime The one-sided barrier problem for gaussian noise

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.441020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a0dea2e5-c45e-466b-a9a2-ea4c85c4fd4a · outbound

This paper cites A framework to characterize performance of lasso algorithms, 2013.

Differentially Private Learning Beyond the Classical Dimensionality Regime A framework to characterize performance of lasso algorithms, 2013

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.423010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9b279d1c-f519-4d9c-ace2-622661fa66b7 · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:20:21.401666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 226eff70-9d7c-41cb-846f-da3a3d76b2ac · outbound

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

Differentially Private Learning Beyond the Classical Dimensionality Regime Precise error analysis of regularized m-estimators in high dimensions

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.382057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 434ca1f0-a47a-488f-8382-8a370cf29663 · outbound

This paper cites Regularized linear regression: A precise analysis of the estimation error.

Differentially Private Learning Beyond the Classical Dimensionality Regime Regularized linear regression: A precise analysis of the estimation error

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.362335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7c4a001-8214-43fe-92a8-d7f5c11c3b00 · outbound

This paper cites an unresolved cited work.

Differentially Private Learning Beyond the Classical Dimensionality Regime Unresolved cited work

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:21.076746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4be4b05-7a98-449d-bb21-722ee8db4941 · outbound

This paper cites High-Dimensional Probability: An Introduction with Applications in Data Science.

Differentially Private Learning Beyond the Classical Dimensionality Regime High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:21.081697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7159d93-8e79-4cf3-adf8-a8594a78add4 · outbound

This paper cites (nearly) optimal private linear regression for sub-gaussian data via adaptive clipping.

Differentially Private Learning Beyond the Classical Dimensionality Regime (nearly) optimal private linear regression for sub-gaussian data via adaptive clipping

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.323198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06f68242-8cc3-4b27-98d4-7413b14e7508 · outbound

This paper cites Wainwright.

Differentially Private Learning Beyond the Classical Dimensionality Regime Wainwright

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:21.093206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bff2ef8-d699-4800-b0db-4e904607ae70 · outbound

This paper cites Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain.

Differentially Private Learning Beyond the Classical Dimensionality Regime Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.292456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2d0e7628-5830-4e12-bff5-950487658378 · outbound

This paper cites Privacy for free: Posterior sampling and stochastic gradient monte carlo.

Differentially Private Learning Beyond the Classical Dimensionality Regime Privacy for free: Posterior sampling and stochastic gradient monte carlo

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.272207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c0e728d9-e959-41d4-9445-de7372766aef · outbound

This paper cites Does SLOPE outperform bridge regression? Information and Inference: A Journal of the IMA , 11(1):1--54, 11 2021.

Differentially Private Learning Beyond the Classical Dimensionality Regime Does SLOPE outperform bridge regression? Information and Inference: A Journal of the IMA , 11(1):1--54, 11 2021

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:21.248395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers