Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning | Paper Citation Record

Observation 93e7e66c-6c15-46ac-9e91-1775520c0199 · outbound

This paper cites Randla-net: Efficient semantic segmentation of large- scale point clouds,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Randla-net: Efficient semantic segmentation of large- scale point clouds,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.705725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8f98218-4d2d-4d25-bd4e-6a2fd7d8c62d · outbound

This paper cites Self-supervised graph transformer on large-scale molecular data,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Self-supervised graph transformer on large-scale molecular data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.966555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9ef5b9fd-f103-4828-84de-1c5efa080d3e · outbound

This paper cites Encapsulating Knowledge in One Prompt.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Encapsulating Knowledge in One Prompt

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:58:53.244889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba84b444-a4f5-4422-b3b2-4ad2f3849d6f · outbound

This paper cites Infobatch: Lossless training speed up by unbiased dynamic data pruning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Infobatch: Lossless training speed up by unbiased dynamic data pruning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.953337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a592cb31-209c-4f7f-81f8-dfe9871ae521 · outbound

This paper cites Dataset pruning: Reducing training data by examining generalization influence,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Dataset pruning: Reducing training data by examining generalization influence,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.940365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df75402d-ac87-4626-a356-a769ff1388f9 · outbound

This paper cites Data pruning via moving-one-sample-out,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data pruning via moving-one-sample-out,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.926875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b93c52e3-8ef6-4f14-8877-d14ab40b11d2 · outbound

This paper cites D2 pruning: Message passing for balancing diversity & difficulty in data pruning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning D2 pruning: Message passing for balancing diversity & difficulty in data pruning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.914668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7e028532-d3b0-4117-8c0a-a8d3e99c305b · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Deepcore: A comprehensive library for coreset selection in deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.901431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a534d7b8-723f-4129-9335-c5301d89563c · outbound

This paper cites Mind the boundary: Coreset selection via reconstructing the deci- sion boundary,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Mind the boundary: Coreset selection via reconstructing the deci- sion boundary,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.889096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d4c3d400-b335-4aa8-8d00-c57dc23fd6b6 · outbound

This paper cites Super-samples from kernel herding,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Super-samples from kernel herding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.877216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 25d719da-ef71-43fd-8715-6b3c47dc2a8b · outbound

This paper cites Contextual diversity for active learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Contextual diversity for active learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.862566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e28675cd-a21d-470a-86b4-bcce451bc42d · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.755205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca04646a-babf-41b9-a826-0101d78f1233 · outbound

This paper cites Filtering, dis- tillation, and hard negatives for vision-language pre-training,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Filtering, dis- tillation, and hard negatives for vision-language pre-training,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.849128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 237a6427-66b3-4c6d-8e2a-5e14bf892086 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.764166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f0d057-d1a0-49d1-b755-8d2ba90baf63 · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Datacomp: In search of the next generation of multimodal datasets,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.836074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e09914f4-de2f-430f-af67-f4ce264c1fe1 · outbound

This paper cites Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.772518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6a67580-67f6-433d-8a9a-4f0282a7559d · outbound

This paper cites Data curation via joint example selection further accelerates multimodal learning.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data curation via joint example selection further accelerates multimodal learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.777421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acb1be78-ea84-45b7-9d8f-da174fadd4a3 · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data prun- ing,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Beyond neural scaling laws: beating power law scaling via data prun- ing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.822900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6452c50f-5c83-4615-83bd-a6c380474a82 · outbound

This paper cites Backups and the right to be forgotten in the gdpr: An uneasy relationship,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Backups and the right to be forgotten in the gdpr: An uneasy relationship,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.810398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6f36c93-683a-4ed9-81e2-62fb02967d0b · outbound

This paper cites Data minimization,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data minimization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.796118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 650b79fa-28b3-4050-9a0e-ca2d4623347b · outbound

This paper cites Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.797102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6049b4fa-a578-441d-a873-62fb48e90965 · outbound

This paper cites Privacy-preserving deep learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Privacy-preserving deep learning,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.801507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e098e59e-8f6f-4ab6-a48e-48d82a72401c · outbound

This paper cites Se- cure, privacy-preserving and federated machine learning in medical imaging,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Se- cure, privacy-preserving and federated machine learning in medical imaging,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.770131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b60b2e9f-c97d-4db0-b7bd-b20e3d890d4c · outbound

This paper cites Membership inference attacks from first principles,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Membership inference attacks from first principles,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.809607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a6e0bab-bdbe-4829-9ce3-d12f06b8ddbf · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Glister: Generalization based data subset selection for efficient and robust learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.813466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a91edaf0-30e0-47ba-a5d7-4cc46651ee9a · outbound

This paper cites Privacy Side Channels in Machine Learning Systems.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Privacy Side Channels in Machine Learning Systems

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.817729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36b7a261-5ca3-4609-8199-8a60d7ce3345 · outbound

This paper cites Privacy for free: How does dataset condensation help privacy?.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Privacy for free: How does dataset condensation help privacy?

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.737707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2f302526-f187-4d16-809d-93ace9d02aa5 · outbound

This paper cites No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy".

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.826571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60bb6b9c-db1a-4a94-b17a-317c7319e918 · outbound

This paper cites Backdoor Attacks Against Dataset Distillation.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Backdoor Attacks Against Dataset Distillation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.831968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750533a0-524c-4e30-8357-6e0474317393 · outbound

This paper cites Slimmable dataset condensation,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Slimmable dataset condensation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.723851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22341957-5362-4499-8232-77a660ff39f2 · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.709112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef157d48-098b-40cc-984d-41c43df0cb03 · outbound

This paper cites Data solutions and audience targeting services,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data solutions and audience targeting services,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.691725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c3e9a900-935d-44b8-9b25-9b73d70568e5 · outbound

This paper cites Data quality and enrichment services,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data quality and enrichment services,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.675649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 867de725-80a9-4faf-8fdd-364c55dba226 · outbound

This paper cites Global database of events, language, and tone,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Global database of events, language, and tone,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.659277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a8262505-75a2-46cb-a0e6-32bfa7cfe302 · outbound

This paper cites The Data Minimization Principle in Machine Learning.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning The Data Minimization Principle in Machine Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.854462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a2b7122-4d29-4325-9432-3fbdde23e623 · outbound

This paper cites Privacy side channels in machine learning systems,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Privacy side channels in machine learning systems,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.642268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 13adcfa1-90c6-4bea-bcbe-86ba57a53b24 · outbound

This paper cites Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.862425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3269164-2d2c-4f6f-8d6e-ec268f49b90b · outbound

This paper cites Membership inference attacks on machine learning: A survey,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Membership inference attacks on machine learning: A survey,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.618919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3386a22b-0df1-40d5-ae2a-6c9fe12171bd · outbound

This paper cites Membership inference attacks against machine learning models,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Membership inference attacks against machine learning models,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.870255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d679016-8d50-4d52-9023-99b90a9d32e8 · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.874004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02298476-b51b-4e81-8fe0-50cbc3963ed9 · outbound

This paper cites Systematic evaluation of privacy risks of machine learning models,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Systematic evaluation of privacy risks of machine learning models,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.878190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e0fef3-b64b-4a57-b6ec-2a7e7d9fa9a1 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Privacy risk in machine learning: Analyzing the connection to overfitting,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.882198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30474d98-affd-4960-93d1-53189175c890 · outbound

This paper cites Dataperf: Benchmarks for data-centric ai development,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Dataperf: Benchmarks for data-centric ai development,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.573690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c012709-911c-4afe-894c-a60593cdf1e9 · outbound

This paper cites Data-centric Artificial Intelligence: A Survey.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data-centric Artificial Intelligence: A Survey

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.890782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d08c7d6e-26be-4da1-9287-6323edcab2d5 · outbound

This paper cites Data- centric artificial intelligence,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Data- centric artificial intelligence,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.561243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de8a6496-fb94-4e8e-8f7d-08d614cd29b2 · outbound

This paper cites Why it’s time for data-centric artificial intelligence,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Why it’s time for data-centric artificial intelligence,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.547870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b82eb068-f8aa-4c74-b70f-c2ae61de2403 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.903156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b08e1f3c-446e-4c30-980b-a33b542c1efa · outbound

This paper cites Im- proving language understanding by generative pre-training,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Im- proving language understanding by generative pre-training,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.523965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8cc73c8b-b03a-4bf9-b2c9-0a33964c8b1d · outbound

This paper cites Language models are unsupervised multitask learners.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Language models are unsupervised multitask learners

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.911293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d08c23f-0fd2-47c2-ab20-a79fb7a63743 · outbound

This paper cites Language models are few-shot learners,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Language models are few-shot learners,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.499147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e4bfc03e-43c5-499e-a665-18c5d87afb14 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Generalizing from a few examples: A survey on few-shot learning,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.921285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd325a36-6b5e-4ee0-a27b-1186fc931201 · outbound

This paper cites Prototypical networks for few- shot learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Prototypical networks for few- shot learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.479116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d469e0b5-4c6a-4080-a854-385fd2362442 · outbound

This paper cites Dataset condensation with gradient matching,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Dataset condensation with gradient matching,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.468285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 375ec96f-ada1-45f6-8cb7-54abae3a7cdd · outbound

This paper cites Dataset condensation with distribution match- ing,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Dataset condensation with distribution match- ing,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.456981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7128d378-2f72-452f-acc6-7c5e97fd09f8 · outbound

This paper cites Membership inference attacks and defenses in classification models,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Membership inference attacks and defenses in classification models,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.936434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c13a5c9-af0c-4318-b80c-7a640976a582 · outbound

This paper cites an unresolved cited work.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:58:53.436600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c0e3d750-d07b-4118-893a-57f37769dffe · outbound

This paper cites The applications of capture-recapture models to epidemiological data,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning The applications of capture-recapture models to epidemiological data,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.424131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3da997e1-16d8-4822-8d61-0e5d365f4ba2 · outbound

This paper cites Cumulative distribution function — wikipedia, the free encyclopedia.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Cumulative distribution function — wikipedia, the free encyclopedia

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.411764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ae922215-40d3-4e13-b99b-756b17424392 · outbound

This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.951483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 397828ef-6797-4b52-8bc1-35a1b1d0f28b · outbound

This paper cites Active Learning by Acquiring Contrastive Examples.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Active Learning by Acquiring Contrastive Examples

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.954947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ac518c-8584-46e7-9b3d-c7a64b10676e · outbound

This paper cites An empirical study of example forgetting dur- ing deep neural network learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning An empirical study of example forgetting dur- ing deep neural network learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.399673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0e36b9a1-9cf1-40fb-8f42-b367fb4d7ff2 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Deep learning on a data diet: Finding important examples early in training,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.961728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4642b918-6bb6-4f6c-ac7a-7ce71c8eff79 · outbound

This paper cites Selection via proxy: Efficient data selection for deep learning,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Selection via proxy: Efficient data selection for deep learning,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.379531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 301a9b91-e774-491a-98e9-4b0d5951e7cc · outbound

This paper cites Coresets for data- efficient training of machine learning models,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Coresets for data- efficient training of machine learning models,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.968142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac301674-601f-45e9-b857-3b9780ba9036 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for effi- cient deep model training,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Grad-match: Gradient matching based data subset selection for effi- cient deep model training,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.360154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f36f7a4b-f52e-4bc4-afe4-0a39fe45e2c5 · outbound

This paper cites Submodular optimization with submod- ular cover and submodular knapsack constraints,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Submodular optimization with submod- ular cover and submodular knapsack constraints,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.348118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 26d55f14-19c3-4d79-a3c4-24e7a4e61b1a · outbound

This paper cites Herding dynamical weights to learn,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Herding dynamical weights to learn,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.979122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44550a3c-7a91-4cad-99d5-cbed963a2736 · outbound

This paper cites an unresolved cited work.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:58:53.325914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bba2939-29cf-41b8-b9b0-bf71e39be391 · outbound

This paper cites Ecological methodology,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Ecological methodology,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.310412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c15a79cb-0c76-4d3f-8679-63b98e238fb0 · outbound

This paper cites Deep learning with differential privacy,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Deep learning with differential privacy,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.989858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 325ae4f5-e100-4ccf-bb09-fe1e817dd29f · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning mixup: Beyond Empirical Risk Minimization

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:52.993418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 994658cb-32f3-4c3a-b8ad-32a359854772 · outbound

This paper cites Enhanced mixup training: a defense method against membership inference attack,.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Enhanced mixup training: a defense method against membership inference attack,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.285718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c7148fd0-36b3-4a4d-a165-1af28b2d716f · outbound

This paper cites an unresolved cited work.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:58:53.273283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dbbb26b-69c6-4e5f-b0dc-c436f5749ba6 · outbound

This paper cites It is the boundary separating the feasible region of optimal trade-offs from the rest of the solution space, highlighting the most efficient solutions in terms of multiple criteria.

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning It is the boundary separating the feasible region of optimal trade-offs from the rest of the solution space, highlighting the most efficient solutions in terms of multiple criteria

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:53.260646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers