Paper Citation Record · LEDGER

Joker: Joint Optimization Framework for Lightweight Kernel Machines

As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.17765.

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

pith.paper-citation-record.v1
2505.17765 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:46:18.828796Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42e55161-788d-45f1-b3c3-4c8d484f538f · outbound

This paper cites write newline.

Joker: Joint Optimization Framework for Lightweight Kernel Machines write newline

Reference 1

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Observation f0003029-a319-4518-a561-356bed74eb8f · outbound

This paper cites Aravkin, Robert Baraldi, and Dominique Orban.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Aravkin, Robert Baraldi, and Dominique Orban

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:28.646371Z

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Observation f6fd0987-a20a-43dd-a3b0-029ce006c16e · outbound

This paper cites Toward large kernel models.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Toward large kernel models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:28.466399Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91f33e71-88e0-42d3-b3a8-49c89b5689b7 · outbound

This paper cites Fast training of large kernel models with delayed projections, November 2024.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Fast training of large kernel models with delayed projections, November 2024

Reference 4

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Observation dba6e507-eb5e-442e-ba8b-cb0212acb7b5 · outbound

This paper cites Convex optimization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Convex optimization

Reference 5

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Observation 291964e0-48a9-4281-8756-dae631b301e8 · outbound

This paper cites First-order methods in optimization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines First-order methods in optimization

Reference 6

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Source-reported events for the cited work

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Observation 03bed6e1-0f0e-42f2-964a-6976a290ad8e · outbound

This paper cites Ellis, Brian Whitman, and Paul Lamere.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Ellis, Brian Whitman, and Paul Lamere

Reference 7

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Source-reported events for the cited work

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Observation 8330d274-99ee-4b98-a993-abae7e28a494 · outbound

This paper cites Baraldi and Drew P.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Baraldi and Drew P

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:27.791177Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e4f4ce5b-d9fb-4d32-89f5-02fa8f3e8681 · outbound

This paper cites Baraldi and Drew P.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Baraldi and Drew P

Reference 9

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Observation 46597af6-2e01-4c39-8449-49e9c14ce63e · outbound

This paper cites Searching for exotic particles in high-energy physics with deep learning.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Searching for exotic particles in high-energy physics with deep learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:27.423886Z

Source-reported events for the cited work

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Observation 1ef38a54-24f9-46d3-b234-be5408baf502 · outbound

This paper cites LIBSVM : A library for support vector machines.

Joker: Joint Optimization Framework for Lightweight Kernel Machines LIBSVM : A library for support vector machines

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:27.257149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae361191-a8ca-4145-a785-97c3bfa562ba · outbound

This paper cites On the algorithmic implementation of multiclass kernel-based vector machines.

Joker: Joint Optimization Framework for Lightweight Kernel Machines On the algorithmic implementation of multiclass kernel-based vector machines

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:27.082754Z

Source-reported events for the cited work

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Observation a556d713-c1de-453c-a3f9-c78558e90f9a · outbound

This paper cites Support- Vector Networks.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Support- Vector Networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:26.801194Z

Source-reported events for the cited work

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Observation 7a5bdefb-2129-4471-bc32-dc720fd3a9c4 · outbound

This paper cites Deep neural tangent kernel and laplace kernel have the same RKHS.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Deep neural tangent kernel and laplace kernel have the same RKHS

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:26.519927Z

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Observation d8e80576-4e25-4ba0-87a0-eeb7757707e2 · outbound

This paper cites ReHLine : Regularized composite ReLU-ReHU loss minimization with linear computation and linear convergence.

Joker: Joint Optimization Framework for Lightweight Kernel Machines ReHLine : Regularized composite ReLU-ReHU loss minimization with linear computation and linear convergence

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:26.257892Z

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Observation edde73c2-2e47-4409-a028-32e4f98abf14 · outbound

This paper cites an unresolved cited work.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Unresolved cited work

Reference 16

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Observation 18982edf-1328-4f6e-aea7-6d5f8eb55f13 · outbound

This paper cites Scalable Kernel Methods via Doubly Stochastic Gradients.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Scalable Kernel Methods via Doubly Stochastic Gradients

Reference 17

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Observation abebeebc-62d5-44d2-9eea-bfb317a6aa50 · outbound

This paper cites Gafni and Dimitri P.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Gafni and Dimitri P

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:25.476467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e0ec359-458a-4bf7-99b7-c3dffcc42652 · outbound

This paper cites Large sample analysis of the median heuristic, October 2018.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Large sample analysis of the median heuristic, October 2018

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:25.246516Z

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Observation e814caf1-adb1-4d28-a653-978fb22495ce · outbound

This paper cites GPyTorch : Blackbox matrix-matrix gaussian process inference with GPU acceleration.

Joker: Joint Optimization Framework for Lightweight Kernel Machines GPyTorch : Blackbox matrix-matrix gaussian process inference with GPU acceleration

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:24.971889Z

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Observation 65d33395-1c6f-4c49-b5ef-405e8ea20fbc · outbound

This paper cites On the similarity between the Laplace and neural tangent kernels.

Joker: Joint Optimization Framework for Lightweight Kernel Machines On the similarity between the Laplace and neural tangent kernels

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:24.793871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6cece96-a62a-40ae-9a27-c79d07d60dd4 · outbound

This paper cites Sathiya Keerthi, and S.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Sathiya Keerthi, and S

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:24.588051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc118894-3704-4c57-87d5-85fa29f9daae · outbound

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

Joker: Joint Optimization Framework for Lightweight Kernel Machines Neural tangent kernel: Convergence and generalization in neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:24.301873Z

Source-reported events for the cited work

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Observation 54b31133-2b35-4717-adb3-521e654f7c2b · outbound

This paper cites an unresolved cited work.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Unresolved cited work

Reference 24

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Observation ad001f34-29eb-48da-a858-9a0bcb56e264 · outbound

This paper cites Stochastic gradient descent for gaussian processes done right.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Stochastic gradient descent for gaussian processes done right

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:23.636192Z

Source-reported events for the cited work

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Observation 7a85a714-c6fe-4d7b-aa5a-5990f65f3ca2 · outbound

This paper cites Diving into the shallows: A computational perspective on large-scale shallow learning.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Diving into the shallows: A computational perspective on large-scale shallow learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:23.368561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8f7064ff-6528-440d-bc67-4fb2de1f9d5e · outbound

This paper cites Kernel machines that adapt to gpus for effective large batch training.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Kernel machines that adapt to gpus for effective large batch training

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:23.149679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3956f080-1c90-49fb-aa05-9500f8e716bf · outbound

This paper cites Globally convergent newton methods for ill-conditioned generalized self-concordant losses.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Globally convergent newton methods for ill-conditioned generalized self-concordant losses

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:23.000511Z

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Observation 3e547053-7469-4394-9e09-b169f40207c4 · outbound

This paper cites Kernel methods through the roof: Handling billions of points efficiently.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Kernel methods through the roof: Handling billions of points efficiently

Reference 29

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Observation 1116379f-a49b-422c-bd5a-62ef9d3fabe2 · outbound

This paper cites Let's make block coordinate descent converge faster: Faster greedy rules, message-passing, active-set complexity, and superlinear convergence.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Let's make block coordinate descent converge faster: Faster greedy rules, message-passing, active-set complexity, and superlinear convergence

Reference 30

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Observation bddc7898-44b4-4389-8c9f-911a8eb76597 · outbound

This paper cites The deep bootstrap framework: Good online learners are good offline generalizers.

Joker: Joint Optimization Framework for Lightweight Kernel Machines The deep bootstrap framework: Good online learners are good offline generalizers

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:22.531704Z

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Observation 1208b71b-5d24-4e0b-b994-a1f4e3cbde15 · outbound

This paper cites Coordinate descent converges faster with the Gauss-Southwell rule than random selection.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Coordinate descent converges faster with the Gauss-Southwell rule than random selection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:22.393167Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b65c9f7-1047-4024-b7e1-fa61ab639e6f · outbound

This paper cites an unresolved cited work.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Unresolved cited work

Reference 33

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Observation e4048aa9-5ec0-444a-8052-922c8ddbf1be · outbound

This paper cites SDNA : Stochastic dual newton ascent for empirical risk minimization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines SDNA : Stochastic dual newton ascent for empirical risk minimization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:22.093528Z

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Observation 1d631682-bf74-495a-91ff-6acf46ba07a1 · outbound

This paper cites On fast leverage score sampling and optimal learning.

Joker: Joint Optimization Framework for Lightweight Kernel Machines On fast leverage score sampling and optimal learning

Reference 35

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10b516a5-ae67-4504-af2b-704e2f1769c7 · outbound

This paper cites FALKON : An optimal large scale kernel method.

Joker: Joint Optimization Framework for Lightweight Kernel Machines FALKON : An optimal large scale kernel method

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:21.789715Z

Source-reported events for the cited work

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Observation 6a994b73-134f-4086-a20f-b0e878dcab1a · outbound

This paper cites Have ASkotch : A neat solution for large-scale kernel ridge regression.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Have ASkotch : A neat solution for large-scale kernel ridge regression

Reference 37

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Source-reported events for the cited work

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Observation a6d7cc8a-bd59-4983-8642-047b0e35e7cd · outbound

This paper cites Random features for large-scale kernel machines.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Random features for large-scale kernel machines

Reference 38

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Observation 7c95a18b-1ef0-4560-934e-fea1920cff84 · outbound

This paper cites Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:21.410818Z

Source-reported events for the cited work

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Observation 338dddd0-eba1-4963-ac56-efe130346e6e · outbound

This paper cites Graphical model structure learning with l1-regularization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Graphical model structure learning with l1-regularization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:21.261263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b6e71c81-061c-4d1f-b237-16e41d84bc23 · outbound

This paper cites Projected newton-type methods in machine learning.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Projected newton-type methods in machine learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:21.100263Z

Source-reported events for the cited work

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Observation 7aeedec4-8bef-4a35-975c-5727ee47ecdc · outbound

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Joker: Joint Optimization Framework for Lightweight Kernel Machines Unresolved cited work

Reference 42

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Observation faf0b462-f277-445f-9188-738d1f983386 · outbound

This paper cites Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:20.687837Z

Source-reported events for the cited work

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Observation bd7bd5e6-ab03-4b0c-a302-369a288f7f6e · outbound

This paper cites The conjugate gradient method and trust regions in large scale optimization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines The conjugate gradient method and trust regions in large scale optimization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:20.404685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b0fc393-2357-478c-a0b4-b919d766f7db · outbound

This paper cites Stochastic dual coordinate ascent methods for regularized loss minimization.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Stochastic dual coordinate ascent methods for regularized loss minimization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:20.111101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7b456dcb-749c-4be3-9600-f3612d0d669b · outbound

This paper cites Large Scale Kernel Learning using Block Coordinate Descent.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Large Scale Kernel Learning using Block Coordinate Descent

Reference 46

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Source-reported events for the cited work

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Observation f4721872-fe8f-49dc-a11e-5912841afb47 · outbound

This paper cites Using the Nystr \"o m method to speed up kernel machines.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Using the Nystr \"o m method to speed up kernel machines

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:19.960570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6fd70ec7-2b42-4ef1-a199-fc41731cc1f3 · outbound

This paper cites ThunderSVM : A fast SVM library on GPUs and CPUs.

Joker: Joint Optimization Framework for Lightweight Kernel Machines ThunderSVM : A fast SVM library on GPUs and CPUs

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:19.759043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ccb7365e-939e-4844-a356-6aa57826382f · outbound

This paper cites Dual coordinate descent methods for logistic regression and maximum entropy models.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Dual coordinate descent methods for logistic regression and maximum entropy models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:19.553076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3bea9239-8853-4439-8b7f-549c34fc7c2c · outbound

This paper cites Nystr \"o m method vs random Fourier features: A theoretical and empirical comparison.

Joker: Joint Optimization Framework for Lightweight Kernel Machines Nystr \"o m method vs random Fourier features: A theoretical and empirical comparison

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:46:19.266449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.