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Paper Citation Record · LEDGER

One-shot Conditional Sampling: MMD meets Nearest Neighbors

As of 20 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 1 inbound Pith citation observation for arXiv:2509.25507.

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

pith.paper-citation-record.v1
2509.25507 v2

Coverage vector

measured 100 of 103 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:48:39.514391Z

measured 101 of 101 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T15:04:00.690647Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 103 outbound references displayed

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  • verified fuzzy34
  • unresolved65
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf1d4b53-cd1c-4349-8df1-96e93c3f7cbe · outbound

This paper cites Neural network learning: Theoretical foundations.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Neural network learning: Theoretical foundations

Reference 1

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Observation 4d756093-950e-4ee7-9811-73490afdfab3 · outbound

This paper cites Towards principled methods for training generative adversarial networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Towards principled methods for training generative adversarial networks

Reference 2

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Observation 792ec60e-8658-477e-9372-196bd76cc3d2 · outbound

This paper cites Theory of reproducing kernels.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Theory of reproducing kernels

Reference 3

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Observation e917eafc-fc61-4132-a8b3-e613d6016031 · outbound

This paper cites A simple measure of conditional dependence.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A simple measure of conditional dependence

Reference 4

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Observation 77504cdb-bf9d-4f15-b829-1a5f27bfa667 · outbound

This paper cites Conditional sampling with monotone gans: From generative models to likelihood-free inference.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional sampling with monotone gans: From generative models to likelihood-free inference

Reference 5

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Observation d20dcbb1-379f-448a-80ff-f0c59c87009e · outbound

This paper cites Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks

Reference 6

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Observation cef51985-793a-4898-baf6-ac2d165a9719 · outbound

This paper cites Demystifying MMD GANs.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Demystifying MMD GANs

Reference 7

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Observation 531cd8c2-47c3-490f-a79c-169fb438e6d8 · outbound

This paper cites Convexity and measures of statistical association.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Convexity and measures of statistical association

Reference 8

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Observation d562f667-573b-43de-a9c4-69aad307fe7a · outbound

This paper cites Concentration inequalities.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Concentration inequalities

Reference 9

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Observation 72ef9468-ba7c-416a-a724-a7b6df663cee · outbound

This paper cites Optional p \'o lya trees: Posterior rates and uncertainty quantification.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Optional p \'o lya trees: Posterior rates and uncertainty quantification

Reference 10

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Observation dd1b63dc-ea05-48c8-a9e8-6611155d17d1 · outbound

This paper cites Boosting the power of kernel two-sample tests.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Boosting the power of kernel two-sample tests

Reference 11

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Observation c4ff6f2c-a2b9-4e7d-b48f-76e1f31bce88 · outbound

This paper cites A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference).

One-shot Conditional Sampling: MMD meets Nearest Neighbors A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)

Reference 12

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Observation e4cf9f8c-5d7c-4928-ba39-8046091fdf9f · outbound

This paper cites Conditional distribution learning on graphs.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional distribution learning on graphs

Reference 13

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Source-reported events for the cited work

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Observation fbf4cc2a-e67f-46ed-921b-22d7ffd0e3fe · outbound

This paper cites The estimation of conditional densities.

One-shot Conditional Sampling: MMD meets Nearest Neighbors The estimation of conditional densities

Reference 14

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Observation 37f16c4d-b55f-4a66-a5cc-ccdae3901ed9 · outbound

This paper cites A kernel test of goodness of fit.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A kernel test of goodness of fit

Reference 15

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Observation 83c28a87-7ce0-4d2a-9c8d-50126d9120e9 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

One-shot Conditional Sampling: MMD meets Nearest Neighbors An analysis of single-layer networks in unsupervised feature learning

Reference 16

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Observation cf479a16-0958-4a88-a4fe-eec6e67be679 · outbound

This paper cites The frontier of simulation-based inference.

One-shot Conditional Sampling: MMD meets Nearest Neighbors The frontier of simulation-based inference

Reference 17

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Observation 639b92c1-9975-42ae-93c4-fc0f7b394f13 · outbound

This paper cites Optimal rates for k-nn density and mode estimation.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Optimal rates for k-nn density and mode estimation

Reference 18

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Observation d6f7c7b7-d357-4f0d-8101-beef9ce31efb · outbound

This paper cites Measuring Association on Topological Spaces Using Kernels and Geometric Graphs.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Measuring Association on Topological Spaces Using Kernels and Geometric Graphs

Reference 19

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Observation a01078f6-932d-468c-b8bc-c614415b1118 · outbound

This paper cites Deep generative image models using a laplacian pyramid of adversarial networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Deep generative image models using a laplacian pyramid of adversarial networks

Reference 20

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Observation 49297289-ef8b-4bb5-8631-a2de49b8b122 · outbound

This paper cites Tutorial on Variational Autoencoders.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Tutorial on Variational Autoencoders

Reference 21

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Observation c93f1923-196e-477a-9df0-6e44bfe8471c · outbound

This paper cites Training generative neural networks via maximum mean discrepancy optimization.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Training generative neural networks via maximum mean discrepancy optimization

Reference 22

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Observation f61a1ff6-6d3c-489d-b563-48543b842a6b · outbound

This paper cites A crossvalidation method for estimating conditional densities.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A crossvalidation method for estimating conditional densities

Reference 23

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Observation 2463533a-4fa8-42ab-bf74-a6a28590caed · outbound

This paper cites Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems

Reference 24

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Observation c99655ca-5b88-483e-aa5d-2545ebcf4301 · outbound

This paper cites An algorithm for finding best matches in logarithmic expected time.

One-shot Conditional Sampling: MMD meets Nearest Neighbors An algorithm for finding best matches in logarithmic expected time

Reference 25

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Observation 1e5e1dd6-e7fd-49e4-bddc-e049d7a4e2a2 · outbound

This paper cites Jensen-shannon divergence and hilbert space embedding.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Jensen-shannon divergence and hilbert space embedding

Reference 26

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Observation e278b925-03ed-4353-98b5-c9a894fb437b · outbound

This paper cites Kernel measures of conditional dependence.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Kernel measures of conditional dependence

Reference 27

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Generative adversarial nets

Reference 28

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One-shot Conditional Sampling: MMD meets Nearest Neighbors A kernel statistical test of independence

Reference 29

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One-shot Conditional Sampling: MMD meets Nearest Neighbors A kernel two-sample test

Reference 30

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional distribution modelling for few-shot image synthesis with diffusion models

Reference 31

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Approximating conditional distribution functions using dimension reduction

Reference 32

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional Image Generation by Conditioning Variational Auto-Encoders

Reference 33

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One-shot Conditional Sampling: MMD meets Nearest Neighbors The elements of statistical learning, 2009

Reference 34

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Observation 9efd0aff-d855-4660-b84e-92688ef92dac · outbound

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Classifier-Free Diffusion Guidance

Reference 35

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Denoising diffusion probabilistic models

Reference 36

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Observation 565e37be-d376-4e42-bf26-dbf33b2b4688 · outbound

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional transformation models

Reference 37

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Observation 107b3ec2-b0ce-43ce-8dc8-8eb6769aeb9d · outbound

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One-shot Conditional Sampling: MMD meets Nearest Neighbors Cosmological constraints from the redshift-space galaxy skew spectra

Reference 38

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Observation b7b84461-8da4-4215-9737-179152ae600f · outbound

This paper cites Kernel partial correlation coefficient---a measure of conditional dependence.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Kernel partial correlation coefficient---a measure of conditional dependence

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ad42fada-915d-40ab-a3ef-e9fba7af42d2 · outbound

This paper cites Evaluating Aleatoric Uncertainty via Conditional Generative Models.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Evaluating Aleatoric Uncertainty via Conditional Generative Models

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.037559Z digest=sha256:0d04ea5f3b3741d1737d66dd5b856532d1c24be5658c172c2b26fb46834dc177

Observation 6b8f4bf8-dfcd-4482-ae69-5bc8404811a1 · outbound

This paper cites Estimating and visualizing conditional densities.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Estimating and visualizing conditional densities

Reference 41

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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 2026-08-20T06:33:59.587034+00:00.

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Observation 7be96f9b-c645-40b9-95ad-fd8556029df2 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Image-to-image translation with conditional adversarial networks

Reference 42

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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 2026-08-20T06:33:59.587034+00:00.

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Observation 5d63236a-67bc-403a-bef2-3cac7eefdb66 · outbound

This paper cites Nonparametric conditional density estimation in a high-dimensional regression setting.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Nonparametric conditional density estimation in a high-dimensional regression setting

Reference 43

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.115204Z digest=sha256:998615385e7f4098dc0f9d1284bee16c56b3780ff2ed008f439a08d936b85941

Observation 0584d7f9-5ba6-48bd-a9da-29fbb8faef3f · outbound

This paper cites Randomized near-neighbor graphs, giant components and applications in data science.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Randomized near-neighbor graphs, giant components and applications in data science

Reference 44

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.136565Z digest=sha256:0fd75a56e4c850a7d9b0d47ff68e7f21b93d0d35450104d94124a57581f99ce8

Observation 8d304233-447a-467c-a7b1-74c04d59fb53 · outbound

This paper cites Foundations of modern probability, volume 2.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Foundations of modern probability, volume 2

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ea613cf1-dae1-49a5-a4dc-21c4280b46ec · outbound

This paper cites Progressive growing of GAN s for improved quality, stability, and variation.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Progressive growing of GAN s for improved quality, stability, and variation

Reference 46

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.146760Z digest=sha256:8ea2bafce625a86b7ccdd9c7146e75805825a9135e9e0e3b7c882c7a529d512e

Observation 876888c3-282a-46c7-a597-7f06233274f9 · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Accurate image super-resolution using very deep convolutional networks

Reference 47

Resolution
unresolved
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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T15:48:38.151173Z digest=sha256:2e105811048289fbf22aff4d6ff70c96024e7c894b39242f03ed699e7119e3f4

Observation 6571d940-1e59-4567-8555-3d4c31be939d · outbound

This paper cites Probability theory: a comprehensive course.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Probability theory: a comprehensive course

Reference 48

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.156179Z digest=sha256:40ec36316d57d409e43d779828810c057f6143fc1d736e415ebddb2b82c6240e

Observation 7477fa32-cbc6-4b2d-85c1-340f333f3f33 · outbound

This paper cites Truly multivariate structured additive distributional regression.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Truly multivariate structured additive distributional regression

Reference 49

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.160150Z digest=sha256:0eb70e4551baacd692a9a6ac542c1ff6f9eeb70e0be2b2e071643731b6ae0f8c

Observation 1fdeb1a4-428e-4bca-abe3-64fbdb3934b0 · outbound

This paper cites Regression quantiles.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Regression quantiles

Reference 50

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.164843Z digest=sha256:9df05fd1d4953f234ee4da81a2d2d574fd9fcf7c2a4e00914b82d80a96d428f4

Observation b1d6d413-9906-464a-8aa3-dc1a76abc36c · outbound

This paper cites On the rate of convergence of fully connected very deep neural network regression estimates.

One-shot Conditional Sampling: MMD meets Nearest Neighbors On the rate of convergence of fully connected very deep neural network regression estimates

Reference 51

Resolution
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source=arxiv_source observed=2026-08-15T15:48:38.169144Z digest=sha256:ab639bf133dea287ad5752235cf872fd29bddbd50331255811a3c912e34300a8

Observation b31f3d59-0e6c-4653-a2cd-4eb563b31b3a · outbound

This paper cites Generative moment matching networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Generative moment matching networks

Reference 52

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.173775Z digest=sha256:8763dd38bdfec3c478001c262bf5586b0b4069dc29f5582a3847382074d3d781

Observation b6dddc0c-1e10-4c94-9b13-ca3c35912819 · outbound

This paper cites Strong consistency of the kernel estimators of conditional density function.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Strong consistency of the kernel estimators of conditional density function

Reference 53

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.234490Z digest=sha256:c0ae65e34406ff07e56a0611df0b4e44aacbdc366f5e75e33a751fd5e55e1ce2

Observation 1954485c-5cf9-4950-a260-4bb0cde37b3b · outbound

This paper cites Validation diagnostics for sbi algorithms based on normalizing flows.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Validation diagnostics for sbi algorithms based on normalizing flows

Reference 54

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.310891Z digest=sha256:8ef17b06cae3bb2b88ceb3e03b87543a680a4d8e4e9c53d36bdef20d47f74d75

Observation 6fd4f3cf-107e-4111-9115-e2651c01e7f0 · outbound

This paper cites Wasserstein Generative Learning of Conditional Distribution.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Wasserstein Generative Learning of Conditional Distribution

Reference 55

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.316206Z digest=sha256:475143543ee94afa76d0814759aab18dca5d9040293faa4c5cb79463c169228a

Observation 4a98a274-35ef-4c37-9505-1701d7c875d9 · outbound

This paper cites Approximating bayes in the 21st century.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Approximating bayes in the 21st century

Reference 56

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.321340Z digest=sha256:0ad463e20035850de9f820f377ef5a6d7960fc943e62d4a9323a435a78f5003e

Observation f20cad47-549b-43d3-823d-9581612c42f5 · outbound

This paper cites Constraining the higgs potential with neural simulation-based inference for di-higgs production.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Constraining the higgs potential with neural simulation-based inference for di-higgs production

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.693366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.326841Z digest=sha256:cd33089201060eea7e94958655f8cddbadc5448e8a38b5c067bbae94991e2152

Observation df03068a-6e48-45a2-932e-8075cfc402b6 · outbound

This paper cites Uniform concentration and symmetrization for weak interactions.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Uniform concentration and symmetrization for weak interactions

Reference 58

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.413965Z digest=sha256:fee040ca4942a19b701570ddc006317d4138c95b34ca9f186b29c773a72ba67c

Observation 55bc5735-5311-4645-a779-51c901079e9e · outbound

This paper cites Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure

Reference 59

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.419488Z digest=sha256:9b5f9d9d76ada239120d9b7ae9e62387e1a8cee9e71adfa8779494fa7a52648e

Observation 05027cf4-992a-48d6-8c96-63727f29137d · outbound

This paper cites Conditional Generative Adversarial Nets.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional Generative Adversarial Nets

Reference 60

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T15:48:38.425266Z digest=sha256:88c6710829dfa2d61e994216389e0b83b57b1bcc3c7c6f3b5898239b9afc9154

Observation 62903e3c-bd3a-457c-bbf9-898ba945622a · outbound

This paper cites A generative model for zero shot learning using conditional variational autoencoders.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A generative model for zero shot learning using conditional variational autoencoders

Reference 61

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.449059Z digest=sha256:7c262f6f132302c0733fa7bde04d5cdbb5ffe164b5bdab0d8ea2d9c27dfb2fa5

Observation b7159e6e-e058-4fdc-aacd-b6fa550a79ad · outbound

This paper cites cGANs with Projection Discriminator.

One-shot Conditional Sampling: MMD meets Nearest Neighbors cGANs with Projection Discriminator

Reference 62

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no resolver link, observed 2026-08-15T15:48:38.516760Z

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source=arxiv_source observed=2026-08-15T15:48:38.516760Z digest=sha256:85fef7a002b18388f9bdc0fd48bc0f0e368db5ae701ab79d6000f630054e412f

Observation 6aaec1ff-34f5-4ff5-9a4b-7b973bdef886 · outbound

This paper cites Integral probability metrics and their generating classes of functions.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Integral probability metrics and their generating classes of functions

Reference 63

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.522334Z digest=sha256:9926e806e9b00b1d0ea012a5febeb4321741dad094517c8b2c8044ce189297c9

Observation 25531937-6bf3-4f7f-b342-56ca8d69452a · outbound

This paper cites Adaptive approximation and generalization of deep neural network with intrinsic dimensionality.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Adaptive approximation and generalization of deep neural network with intrinsic dimensionality

Reference 64

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no resolver link, observed 2026-08-15T15:48:38.527940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.527940Z digest=sha256:5cae83c12ae6fedde63c26127597c6e811883bae3fc07fa81d839dd746c57118

Observation 0ab0ae27-81f5-45f3-87f6-d166b8459566 · outbound

This paper cites A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics

Reference 65

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.626415Z digest=sha256:a1e0ea03378291b456132e9c97ec2463fdf5f9b7cd4b9e004a50fbe1b801e7c4

Observation cdca56e9-894d-45b3-a587-f1e51181ecc9 · outbound

This paper cites Conditional image synthesis with auxiliary classifier gans.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional image synthesis with auxiliary classifier gans

Reference 66

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verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.438225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.632910Z digest=sha256:ab2038a68ec4edef8f08b3158049a7ade3499481e7f7ae12ae3a2b425bc2c14b

Observation 663c05bd-005d-4d66-94b0-1934a61b54f8 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Normalizing flows for probabilistic modeling and inference

Reference 67

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no resolver link, observed 2026-08-15T15:48:38.709924Z

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source=arxiv_source observed=2026-08-15T15:48:38.709924Z digest=sha256:c9ef2e7add974c53ec94d45cd91699bb05465054b2296d28bcaadc2ac9d6f673

Observation ded9639b-d117-461f-9ea6-c15a0d8b335d · outbound

This paper cites A measure-theoretic approach to kernel conditional mean embeddings.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A measure-theoretic approach to kernel conditional mean embeddings

Reference 68

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.716112Z digest=sha256:416bfcada3695d6b106d31302bd5903ff5747e9c15ded5a5ea8c17648571c362

Observation c83a5faf-c4c8-462e-b02a-fdece5a65d4c · outbound

This paper cites Gatsbi: Generative adversarial training for simulation-based inference.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Gatsbi: Generative adversarial training for simulation-based inference

Reference 69

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verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.327217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.721368Z digest=sha256:92c04a51491e5fa5590cd67b3378b6e158973f7728f2672fca57c1065640dce5

Observation bb545b22-958f-418c-bdf7-deecd309d262 · outbound

This paper cites Methods of modern mathematical physics: Functional analysis, volume 1.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Methods of modern mathematical physics: Functional analysis, volume 1

Reference 70

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verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.230205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.726211Z digest=sha256:b234b10a10da70d1efab4eafcf13a7236af4e90bc7f59a86dead5737a8770503

Observation fd848187-0953-4113-a35d-27eeb3e9785c · outbound

This paper cites Generative adversarial text to image synthesis.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Generative adversarial text to image synthesis

Reference 71

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verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.213811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.771619Z digest=sha256:424bacf1d28c113889a5b32258215851785f0a9e0364423af42a2af65d1cd3ca

Observation d89ec23a-c02f-45ae-b9ff-ef14075f3cf0 · outbound

This paper cites Sufficient dimension reduction via bayesian mixture modeling.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Sufficient dimension reduction via bayesian mixture modeling

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:48:41.096461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.813552Z digest=sha256:a141d5a35e4d36f875d0d0111f1d4168e59d2e3bdc5571a6c127702483cec842

Observation b4ddd2ed-6060-45ea-80c5-36f844c3504a · outbound

This paper cites Conditional generative moment-matching networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional generative moment-matching networks

Reference 73

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.818947Z digest=sha256:0bb03053b08f582bde361afbc5611df6d15abf645c8d12310fa2012cdc897ae1

Observation 5253d926-f574-437e-bc17-87ba3d36e5b3 · outbound

This paper cites Variational inference with normalizing flows.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Variational inference with normalizing flows

Reference 74

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no resolver link, observed 2026-08-15T15:48:38.824986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:48:38.824986Z digest=sha256:5db806778b8c44eaaf480d3e51d609eb53caf9cec3bda8c8df1191e58226f51f

Observation dc7b3ebd-c5ce-40e4-a119-c887c84f2c79 · outbound

This paper cites Generalized additive models for location, scale and shape.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Generalized additive models for location, scale and shape

Reference 75

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verified fuzzy
raw_fallback, observed 2026-08-15T15:48:40.972529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:38.830238Z digest=sha256:f5ba1842971fe3bf3156553d2be4bc8e904c8ba0b539b08be01c98f24c2a3173

Observation d0ae9e89-e880-45cd-98cd-41d726ffb0c0 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

One-shot Conditional Sampling: MMD meets Nearest Neighbors High-resolution image synthesis with latent diffusion models

Reference 76

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Observation 8325b968-d9aa-4812-9717-11c4cebd3a27 · outbound

This paper cites Conditional probability density and regression estimators.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional probability density and regression estimators

Reference 77

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source=arxiv_source observed=2026-08-15T15:48:38.956986Z digest=sha256:289972f622239bd82372b888d77418fa597776f3937dd8a6b7005d4a57532df5

Observation a5a6d1a6-0ecc-456b-b0b3-d82c463dbed5 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Photorealistic text-to-image diffusion models with deep language understanding

Reference 78

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Observation 15f878d5-aac5-4c76-bc7a-fdb15db99282 · outbound

This paper cites Improved techniques for training gans.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Improved techniques for training gans

Reference 79

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source=arxiv_source observed=2026-08-15T15:48:38.967576Z digest=sha256:ad32f56238dd19d0f272f4ded55a01ba0694ba0440ca1cb6e732d0e5b25f97c6

Observation f1aa3198-bcab-4398-a197-6b0722d7de0c · outbound

This paper cites Nonparametric regression using deep neural networks with ReLU activation function.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Nonparametric regression using deep neural networks with ReLU activation function

Reference 80

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source=arxiv_source observed=2026-08-15T15:48:38.972657Z digest=sha256:5542251620fe0b563a1c85f8266944b3f43bc9fbad4dc2b069242d4d2583e52a

Observation 04cb66c1-a255-4d65-b90f-03d19423e348 · outbound

This paper cites Ksd aggregated goodness-of-fit test.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Ksd aggregated goodness-of-fit test

Reference 81

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source=arxiv_source observed=2026-08-15T15:48:39.072579Z digest=sha256:afd47f44e2ad3f5514dfd2dbd899699e04b85c1b67719f7124f770c8c5b487e2

Observation e949e244-5b42-4c0d-a5e5-1f980cd6e98f · outbound

This paper cites Mmd aggregated two-sample test.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Mmd aggregated two-sample test

Reference 82

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source=arxiv_source observed=2026-08-15T15:48:39.154860Z digest=sha256:1e4a6b3fe3c12601b240b6a3cabf3ce4d44e92fd96a974f1f22ffd3990a68c49

Observation acda5c09-cfb8-4de7-a40e-208a8db29671 · outbound

This paper cites Deep network approximation characterized by number of neurons.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Deep network approximation characterized by number of neurons

Reference 83

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Observation 756c2fa5-5009-459f-8099-83c553e40cf0 · outbound

This paper cites Hilbert space embeddings of conditional distributions with applications to dynamical systems.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Hilbert space embeddings of conditional distributions with applications to dynamical systems

Reference 84

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T15:48:39.266023Z digest=sha256:92fbb32b873c54e08c6dd4da5b729103b74240f9c4b2307db7124b95baa0e2d1

Observation 784648ed-a253-4f46-aac7-daa007353a32 · outbound

This paper cites Wasserstein generative regression.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Wasserstein generative regression

Reference 85

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source=arxiv_source observed=2026-08-15T15:48:39.271711Z digest=sha256:2c99cb07539cc441567b389ded618ca2430f2bef9eb930107adefd7a6dc07cd6

Observation c85eb789-86ce-46d4-9372-f3fe3631ba18 · outbound

This paper cites Hilbert space embeddings and metrics on probability measures.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Hilbert space embeddings and metrics on probability measures

Reference 86

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source=arxiv_source observed=2026-08-15T15:48:39.276620Z digest=sha256:1082ba0246ca085e4facde32250c21aa1f73a837832fff1104bda64395fa49ed

Observation 43bd1150-bf1a-4700-b78c-90749ed6354a · outbound

This paper cites Universality, characteristic kernels and rkhs embedding of measures.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Universality, characteristic kernels and rkhs embedding of measures

Reference 87

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source=arxiv_source observed=2026-08-15T15:48:39.281362Z digest=sha256:022aa96568de93516c78a3e3429179a713127e5cd821d7bd83614917ba994d7a

Observation 86fad6f5-8152-4ecf-9cd0-abef0213a046 · outbound

This paper cites Least-squares conditional density estimation.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Least-squares conditional density estimation

Reference 88

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source=arxiv_source observed=2026-08-15T15:48:39.287001Z digest=sha256:0d91e4bff6ddd29dded9c1cc83126817514990e58cf50344722c7cebc1cb3d40

Observation 8cd024c7-312f-4d6d-a251-7512e54ffe2e · outbound

This paper cites Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

Reference 89

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source=arxiv_source observed=2026-08-15T15:48:39.311420Z digest=sha256:5967f3683119b4c4a81bafa4a3abc7dc19efe5133f6a132dd67937c251c2a1a9

Observation 48901153-4f59-469d-8695-db539f52c033 · outbound

This paper cites Weak convergence.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Weak convergence

Reference 90

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source=arxiv_source observed=2026-08-15T15:48:39.374342Z digest=sha256:9037d4ccb18c49e9c8f2b46e9749d1d223b23664c0ef9b97b7355c6b67513ae9

Observation 1e99c39b-6f5d-44cc-ad00-a10fa15968fc · outbound

This paper cites Optimal transport: old and new, volume 338.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Optimal transport: old and new, volume 338

Reference 91

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source=arxiv_source observed=2026-08-15T15:48:39.379057Z digest=sha256:260aa5e3f59138bdfabe8c7d5cca68cea8378a7dccfb017fba5a885e21d137d2

Observation c5c1cebd-c395-499b-8c56-d264547bc2c6 · outbound

This paper cites Sub-weibull distributions: Generalizing sub-gaussian and sub-exponential properties to heavier tailed distributions.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Sub-weibull distributions: Generalizing sub-gaussian and sub-exponential properties to heavier tailed distributions

Reference 92

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source=arxiv_source observed=2026-08-15T15:48:39.383943Z digest=sha256:d23495d02f6e454a0a798ad0ac768aa494b419543c0912ebedeef82d1bf705dc

Observation 769e7a5b-a1d4-4d03-bf5c-cbc8f57931dc · outbound

This paper cites Mental speed is high until age 60 as revealed by analysis of over a million participants.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Mental speed is high until age 60 as revealed by analysis of over a million participants

Reference 93

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raw_fallback, observed 2026-08-15T15:48:40.428121Z

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source=arxiv_source observed=2026-08-15T15:48:39.389064Z digest=sha256:61a07c7841114bf6951e3351b57f1cbe2d7202bd27b8bd5a24867e24cc1e984a

Observation 5a663855-5164-449d-bfc7-fafcc371adda · outbound

This paper cites Scattered data approximation, volume 17.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Scattered data approximation, volume 17

Reference 94

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source=arxiv_source observed=2026-08-15T15:48:39.393720Z digest=sha256:ab69beee72a078e67ed260f72125a38e0e68040ff9fb551663f57052fc5a1df9

Observation 42496b54-62f5-4bf2-8340-046f2d2b68dd · outbound

This paper cites Mnist handwritten digit database.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Mnist handwritten digit database

Reference 95

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raw_fallback, observed 2026-08-15T15:48:40.275495Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T15:48:39.427003Z digest=sha256:ea767717dad11ba19f3194b6aa2608010bfcd2d99bf77ebfb03f78d238350730

Observation 4ed9b305-c04a-461e-bc68-a6325e83a5b1 · outbound

This paper cites Neural methods for amortized inference.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Neural methods for amortized inference

Reference 96

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source=arxiv_source observed=2026-08-15T15:48:39.435284Z digest=sha256:322f3d68a07b46e526b76527ff827dc44febe297e6d3f0b3fb6105286cd7d472

Observation f55f594c-98f0-4e67-8e3b-fe56ca499249 · outbound

This paper cites Conditional Image Synthesis with Diffusion Models: A Survey.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Conditional Image Synthesis with Diffusion Models: A Survey

Reference 97

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source=arxiv_source observed=2026-08-15T15:48:39.440152Z digest=sha256:518d5fb57b3657b52dafa011e7caa8cae32008376d7f5a4b17f3cf00f88d6b75

Observation 375300a0-eb3f-4e7c-a6f1-08732718f581 · outbound

This paper cites Deep network approximation: Achieving arbitrary accuracy with fixed number of neurons.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Deep network approximation: Achieving arbitrary accuracy with fixed number of neurons

Reference 98

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source=arxiv_source observed=2026-08-15T15:48:39.445401Z digest=sha256:517d8c2182388ce94702af3d71f4d1ee95a14713869e172287a81d47b84eae07

Observation 8f0b916e-cc95-4ccb-9ddf-891bc11e4f7d · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

One-shot Conditional Sampling: MMD meets Nearest Neighbors Image super-resolution using very deep residual channel attention networks

Reference 99

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source=arxiv_source observed=2026-08-15T15:48:39.451085Z digest=sha256:975bab1ce125f7c21badfdea129f75e3772e0273fb87e0920386d4c252e188bf

Observation 4c30de93-0304-406b-a024-bfbf6aae4c34 · outbound

This paper cites A deep generative approach to conditional sampling.

One-shot Conditional Sampling: MMD meets Nearest Neighbors A deep generative approach to conditional sampling

Reference 100

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source=arxiv_source observed=2026-08-15T15:48:39.514391Z digest=sha256:964149e343a21dc3d6adc9be900c9c1d5022385f022b24e5aff5a305f777b0f6

Pith citing papers

Observation 073dc7e3-faeb-4aef-99e6-576ec026d842 · inbound

Conditional Mean Independence and Global Sensitivity Analysis using Nearest Neighbor Graphs cites this paper.

Conditional Mean Independence and Global Sensitivity Analysis using Nearest Neighbor Graphs One-shot Conditional Sampling: MMD meets Nearest Neighbors

Reference 8

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