Paper Citation Record · LEDGER

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2506.07088.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.07088 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:53:43.538288Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T17:43:49.052341Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T23:57:28.100834Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e808972-1afa-4617-b884-468850b60361 · outbound

This paper cites Adapting the linearised laplace model evidence for modern deep learning.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Adapting the linearised laplace model evidence for modern deep learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:55.187807Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5c36f58-6bc8-4b30-8d7d-bdc2a87551f2 · outbound

This paper cites Model-based reinforcement learning with value-targeted regression.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Model-based reinforcement learning with value-targeted regression

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:54.757070Z

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Observation cd8572d4-d147-41b5-871e-32a1a7039de7 · outbound

This paper cites Weight uncertainty in neural network.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Weight uncertainty in neural network

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:54.376133Z

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Observation b40f629b-a2fc-4e7d-aa9c-59154b572334 · outbound

This paper cites Concentration inequalities using the entropy method.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Concentration inequalities using the entropy method

Reference 4

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Observation 30468c94-9049-490c-8f1f-a6a466eff185 · outbound

This paper cites Concentration inequalities: A nonasymptotic theory of independence.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Concentration inequalities: A nonasymptotic theory of independence

Reference 5

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Observation cdf0272c-b2a9-41db-8009-d6b3b088d8a2 · outbound

This paper cites On kernelized multi-armed bandits.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares On kernelized multi-armed bandits

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:53.775662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b89efdf9-bfc6-403d-a5ce-f1947ce2d63a · outbound

This paper cites Distribution-free uncertainty quantification for kernel methods by gradient perturbations.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Distribution-free uncertainty quantification for kernel methods by gradient perturbations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:53.409361Z

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Observation 4364bee4-339f-46a3-b7ad-7161628b2b1c · outbound

This paper cites A simple convergence proof of adam and adagrad.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares A simple convergence proof of adam and adagrad

Reference 8

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Observation 00a9c5a5-0269-4baa-9a77-e5fc40e38b60 · outbound

This paper cites Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping

Reference 9

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Observation 37bf3ecb-e1c1-44fe-baa2-e357fb5a8b44 · outbound

This paper cites An introduction to the bootstrap.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares An introduction to the bootstrap

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:52.775887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb22d87b-b0cd-41d5-a3fb-65557737adbc · outbound

This paper cites 'In-Between' Uncertainty in Bayesian Neural Networks.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares 'In-Between' Uncertainty in Bayesian Neural Networks

Reference 11

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Observation cd1ced8a-fc54-47c1-95bf-25b9484abd67 · outbound

This paper cites Practical contextual bandits with regression oracles.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Practical contextual bandits with regression oracles

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:52.399990Z

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Observation 4e79126b-96f7-4bc6-bdff-47d9568b6d74 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:52.047149Z

Source-reported events for the cited work

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Observation 5f1d2ad2-d13a-44e4-a547-3a8b4763e06d · outbound

This paper cites Stochastic first- and zeroth-order methods for nonconvex stochastic programming.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Stochastic first- and zeroth-order methods for nonconvex stochastic programming

Reference 14

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Observation 814079c2-286c-452b-84ef-60ae7a08ce8e · outbound

This paper cites A distribution-free theory of nonparametric regression.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares A distribution-free theory of nonparametric regression

Reference 15

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Source-reported events for the cited work

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Observation f3faaae1-6637-4957-83fa-e748c2048c58 · outbound

This paper cites Inequalities on the lambert w function and hyperpower function.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Inequalities on the lambert w function and hyperpower function

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:50.922752Z

Source-reported events for the cited work

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Observation 901595f2-c56a-4f5f-b1a6-93398eda5bfb · outbound

This paper cites Improving predictions of bayesian neural nets via local linearization.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Improving predictions of bayesian neural nets via local linearization

Reference 17

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Source-reported events for the cited work

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Observation 39cb8d42-81ff-4aa3-af5c-485630153d0b · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Neural tangent kernel: Convergence and generalization in neural networks

Reference 18

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Observation 65c36eba-1aa1-4148-92f9-931f6be078db · outbound

This paper cites Online sub-sampling for reinforcement learning with general function approximation.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Online sub-sampling for reinforcement learning with general function approximation

Reference 19

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Observation 28160ee3-e9db-4e6a-ba73-3af260786d3e · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 20

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Source-reported events for the cited work

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Observation 6a4479e6-f10a-42b3-aa34-74e3efcdd62d · outbound

This paper cites A lower bound for linear and kernel regression with adaptive covariates.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares A lower bound for linear and kernel regression with adaptive covariates

Reference 21

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3443976b-9518-4aab-ad68-66d785793bb9 · outbound

This paper cites Bandit algorithms.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Bandit algorithms

Reference 22

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Observation 99dabf04-9dd4-44ae-a06c-af0d80424dbd · outbound

This paper cites Convergence of adam under relaxed assumptions.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Convergence of adam under relaxed assumptions

Reference 23

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Source-reported events for the cited work

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Observation f8ad7157-af41-44f8-bd14-193c5b276ec5 · outbound

This paper cites Decoupled weight decay regularization.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Decoupled weight decay regularization

Reference 24

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Observation 59f06c6f-e740-4d96-8513-f9a4afc828c1 · outbound

This paper cites Bayesian Methods for Adaptive Models.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Bayesian Methods for Adaptive Models

Reference 25

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Observation a4b8395b-a7db-471c-885e-4522c228da7d · outbound

This paper cites Stochastic gradient markov chain monte carlo.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Stochastic gradient markov chain monte carlo

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:48.506638Z

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Observation 605debaf-1744-486a-b6de-a2002d1e8bc7 · outbound

This paper cites Deep exploration via bootstrapped dqn.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Deep exploration via bootstrapped dqn

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:48.105830Z

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Observation 3d6a028c-8757-46bd-86e2-10b4cf66c5ae · outbound

This paper cites Randomized prior functions for deep reinforcement learning.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Randomized prior functions for deep reinforcement learning

Reference 28

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Source-reported events for the cited work

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Observation 26816c2b-0dcf-4933-a3b8-f0fb686efa03 · outbound

This paper cites Epistemic neural networks.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Epistemic neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:47.456365Z

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Observation d2a988b5-fb68-41f6-9779-d21313b2dcd1 · outbound

This paper cites Fast exact multiplication by the hessian.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Fast exact multiplication by the hessian

Reference 30

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Observation 54a8ec51-ddae-4f4b-9ace-0ef1e214efef · outbound

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Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Probabilistic methods in the geometry of banach spaces

Reference 31

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Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Unresolved cited work

Reference 32

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Observation 9a775719-2bae-46fd-bb64-d376f05806e9 · outbound

This paper cites Learning to optimize via posterior sampling.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Learning to optimize via posterior sampling

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:46.389269Z

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Observation 1fbaa5e6-99ec-4e3d-9632-14bd4e9b32a4 · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Understanding machine learning: From theory to algorithms

Reference 34

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Observation 78e22285-7746-4eb7-be0d-b552e80b53d1 · outbound

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Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Mathematical statistics

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:45.943208Z

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Observation e11cef2f-c43c-464b-9b0e-206658712710 · outbound

This paper cites Gaussian process optimization in the bandit setting: no regret and experimental design.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Gaussian process optimization in the bandit setting: no regret and experimental design

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:45.596062Z

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Observation 6feb02f6-8f44-424d-8b4a-4c2b4a7f2ccf · outbound

This paper cites An introduction to matrix concentration inequalities.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares An introduction to matrix concentration inequalities

Reference 37

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Observation 14030c39-7a98-4ddb-babd-886cb1ddd3b8 · outbound

This paper cites Asymptotic statistics, volume 3.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Asymptotic statistics, volume 3

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:45.230707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7a8d635-f02f-4e2f-b104-487dffc9b5fc · outbound

This paper cites High-dimensional statistics: A non-asymptotic viewpoint, volume 48.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares High-dimensional statistics: A non-asymptotic viewpoint, volume 48

Reference 39

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Observation a7b6978b-020f-42ad-aaeb-551e37f78334 · outbound

This paper cites All of statistics: a concise course in statistical inference, volume 26.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares All of statistics: a concise course in statistical inference, volume 26

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:44.933044Z

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Observation 7fe754e8-ed93-4038-8297-ee994f429d20 · outbound

This paper cites A decision-theoretic approach to interval estimation.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares A decision-theoretic approach to interval estimation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:44.580909Z

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Observation f1067ef8-3787-420f-a0a8-a0582bb3061d · outbound

This paper cites Online Learning for Linearly Parametrized Control Problems.

Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Online Learning for Linearly Parametrized Control Problems

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:44.215991Z

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Observation 740b7d8c-06a1-4e68-a5e4-b34b074fdee5 · outbound

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Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares Towards understanding convergence and generalization of adamw

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:53:43.840174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation d2fed142-fb32-44b5-963d-e64e114bbb8c · inbound

Generalization in Nonlinear Least Squares via Learned Feature Geometry cites this paper.

Generalization in Nonlinear Least Squares via Learned Feature Geometry Pointwise confidence estimation in the non-linear $\ell^2$-regularized least squares

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:57:28.102410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.