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Paper Citation Record · LEDGER

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000

As of 9 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.06619.

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

pith.paper-citation-record.v1
2507.06619 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:05:17.797748Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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  • unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f463970b-80f2-482b-ad2b-144402685939 · outbound

This paper cites Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy,

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c2761cae-5570-4d0e-abd5-1a7839d9d7c8 · outbound

This paper cites Reconciling privacy and accuracy in ai for medical imaging,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Reconciling privacy and accuracy in ai for medical imaging,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T19:05:18.412322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9b107702-c014-4d67-b971-382d18f8b13f · outbound

This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 718186b1-6e44-4d16-be2e-7e053b5a4557 · outbound

This paper cites Differential Privacy Under Class Imbalance: Methods and Empirical Insights.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Differential Privacy Under Class Imbalance: Methods and Empirical Insights

Reference 4

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verified exact
local_arxiv, observed 2026-08-06T19:05:17.890516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c893c58a-65a4-47ce-bc28-afd4362246f2 · outbound

This paper cites Adaptive privacy preserving deep learning algorithms for medical data,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Adaptive privacy preserving deep learning algorithms for medical data,

Reference 5

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raw_fallback, observed 2026-08-06T19:05:18.391909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9a85f2e1-a466-4e95-8f2f-87153471a716 · outbound

This paper cites A study on adaptive gradient clipping algorithms for differential privacy: Enhancing cyber security and trust,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 A study on adaptive gradient clipping algorithms for differential privacy: Enhancing cyber security and trust,

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-06T19:05:18.339178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6a3c5f10-5ec4-4f92-bb63-14db3fc09ff3 · outbound

This paper cites Deep learning with differential privacy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Deep learning with differential privacy,

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d39203f1-e736-49d1-ba3f-98b70666b193 · outbound

This paper cites Learning rate adaptation for differentially private learning,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Learning rate adaptation for differentially private learning,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:05:18.098644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 21de7318-90b5-49b2-b856-899f44b21ac4 · outbound

This paper cites Dynamic Differential-Privacy Preserving SGD.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Dynamic Differential-Privacy Preserving SGD

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T19:05:17.755433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bcef9b9c-b52f-4140-a839-a5b621f0cdec · outbound

This paper cites On the convergence and calibration of deep learning with differential privacy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 On the convergence and calibration of deep learning with differential privacy,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:05:17.994051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2904f932-6c77-4161-99db-0d17dbab8241 · outbound

This paper cites Disparate Impact in Differential Privacy from Gradient Misalignment.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Disparate Impact in Differential Privacy from Gradient Misalignment

Reference 11

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no resolver link, observed 2026-08-06T19:05:17.762881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 906ced31-2e2c-4c73-b943-c184782ea344 · outbound

This paper cites Dpadamod agc: Adaptive gradient clipping-based differential privacy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Dpadamod agc: Adaptive gradient clipping-based differential privacy,

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-06T19:05:17.973864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation af75b17d-e38e-4506-acbd-f0b3ad897f93 · outbound

This paper cites Differential privacy has disparate impact on model accuracy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Differential privacy has disparate impact on model accuracy,

Reference 13

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raw_fallback, observed 2026-08-06T19:05:17.958389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d18c9508-8c07-4e85-8ef0-3c82fc272aeb · outbound

This paper cites Diversity in Faces.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Diversity in Faces

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 79b637ad-da0c-477f-8d26-3b9d75ad5313 · outbound

This paper cites Privacy in Deep Learning: A Survey.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Privacy in Deep Learning: A Survey

Reference 15

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Unavailable: canonical work link unavailable.

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Observation 1594a3b4-52ed-4e04-9913-025f6497de04 · outbound

This paper cites Demographic Dialectal Variation in Social Media: A Case Study of African-American English.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Demographic Dialectal Variation in Social Media: A Case Study of African-American English

Reference 16

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Unavailable: canonical work link unavailable.

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Observation d0dc8544-a896-4281-94c9-b8746898e45b · outbound

This paper cites The inaturalist species classifi- cation and detection dataset,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 The inaturalist species classifi- cation and detection dataset,

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-06T19:05:17.944988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 56ddc79e-8b52-442f-a34a-0a7719b6f00f · outbound

This paper cites Differential privacy: A survey of results,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Differential privacy: A survey of results,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f1356484-d126-4906-b959-f65151dc0328 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Calibrating noise to sensitivity in private data analysis,

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation be0c2c8d-1619-462f-b757-0202e3e6f9eb · outbound

This paper cites R ´enyi divergence and kullback-leibler divergence,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 R ´enyi divergence and kullback-leibler divergence,

Reference 20

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raw_fallback, observed 2026-08-06T19:05:17.909117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4d73cf72-6cfd-4470-bdbb-a0f6f9e0f78b · outbound

This paper cites R ´enyi differential privacy,.

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 R ´enyi differential privacy,

Reference 21

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.