What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models | Paper Citation Record

Observation 0e897e24-947d-427e-868e-dd17ccceac18 · outbound

This paper cites Attention is all you need.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Attention is all you need

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This paper cites Denoising diffusion probabilistic models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Denoising diffusion probabilistic models

Reference 2

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This paper cites Score- based generative modeling through stochastic differential equations.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Score- based generative modeling through stochastic differential equations

Reference 3

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This paper cites Synthetic Data -- what, why and how?.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Synthetic Data -- what, why and how?

Reference 4

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This paper cites Revisiting classifier two-sample tests.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Revisiting classifier two-sample tests

Reference 5

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This paper cites How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models

Reference 6

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Observation 0f291dc8-1eee-4caf-93f0-3a6bc21e9c38 · outbound

This paper cites Explainable generative Ai (GenXAI): A survey, conceptualization, and research agenda.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Explainable generative Ai (GenXAI): A survey, conceptualization, and research agenda

Reference 7

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This paper cites beta-V AE: Learning basic visual concepts with a constrained variational frame- work.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models beta-V AE: Learning basic visual concepts with a constrained variational frame- work

Reference 8

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Observation 5c5501e6-c9e2-4ea9-b356-4dc685b185c1 · outbound

This paper cites InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets

Reference 9

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Observation d7fcfc3e-1364-4db8-bb1e-68c523a91922 · outbound

This paper cites Trade-offs in fine-tuned diffusion models between accuracy and interpretability.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Trade-offs in fine-tuned diffusion models between accuracy and interpretability

Reference 10

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Observation 8d2272b5-4760-4033-b6d6-be0ba16e8cc8 · outbound

This paper cites Quantifying attention flow in transformers.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Quantifying attention flow in transformers

Reference 11

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This paper cites A multiscale visualization of attention in the transformer model.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A multiscale visualization of attention in the transformer model

Reference 12

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This paper cites xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

Reference 13

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This paper cites Activation maximization generative adversarial nets.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Activation maximization generative adversarial nets

Reference 14

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This paper cites Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Reference 15

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This paper cites Katsaggelos.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Katsaggelos

Reference 16

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Observation 7dfb8d83-f0bb-459b-9573-60fc674d36be · outbound

This paper cites Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Reference 17

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This paper cites Explaining image classifiers by removing input features using generative models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Explaining image classifiers by removing input features using generative models

Reference 18

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Observation f9127798-e52d-4891-8c99-60e0498d84f7 · outbound

This paper cites CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual gans.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual gans

Reference 19

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Observation a7046593-af36-4676-bebe-c0d8aab52813 · outbound

This paper cites CountARFactuals – generating plausible model-agnostic counterfactual explanations with adversarial random forests.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models CountARFactuals – generating plausible model-agnostic counterfactual explanations with adversarial random forests

Reference 20

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This paper cites MCCE: Monte carlo sampling of valid and realistic counterfactual explanations for tabular data.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models MCCE: Monte carlo sampling of valid and realistic counterfactual explanations for tabular data

Reference 21

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This paper cites Tabular data generation: Can we fool XGBoost ? In NeurIPS 2022 First Table Representation Workshop, 2022.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Tabular data generation: Can we fool XGBoost ? In NeurIPS 2022 First Table Representation Workshop, 2022

Reference 22

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This paper cites CIFAKE: Image classification and explainable identification of AI-generated synthetic images.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models CIFAKE: Image classification and explainable identification of AI-generated synthetic images

Reference 23

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This paper cites Detecting deepfake images using deep learning techniques and explainable AI methods.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Detecting deepfake images using deep learning techniques and explainable AI methods

Reference 24

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What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Unresolved cited work

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This paper cites Learning in Implicit Generative Models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Learning in Implicit Generative Models

Reference 26

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This paper cites Auto-encoding variational bayes.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Auto-encoding variational bayes

Reference 27

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This paper cites Generative adversarial nets.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Generative adversarial nets

Reference 28

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This paper cites Variational inference with normalizing flows.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Variational inference with normalizing flows

Reference 29

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This paper cites A neural probabilistic language model.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A neural probabilistic language model

Reference 30

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What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Unresolved cited work

Reference 31

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This paper cites Modeling tabular data using conditional GAN.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Modeling tabular data using conditional GAN

Reference 32

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This paper cites TabDDPM: Modelling tabular data with diffusion models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models TabDDPM: Modelling tabular data with diffusion models

Reference 33

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Observation 14749384-647c-4cd4-92ca-bf82fff4ec70 · outbound

This paper cites TabuLa: Harnessing Language Models for Tabular Data Synthesis.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models TabuLa: Harnessing Language Models for Tabular Data Synthesis

Reference 34

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This paper cites Raab, and Chris Dibben.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Raab, and Chris Dibben

Reference 35

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This paper cites Adversarial random forests for density esti- mation and generative modeling.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Adversarial random forests for density esti- mation and generative modeling

Reference 36

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This paper cites Synthcity: facilitating innovative use cases of synthetic data in different data modalities.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Synthcity: facilitating innovative use cases of synthetic data in different data modalities

Reference 37

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Observation 8569010a-2c27-4e08-b25c-0135dd48e61c · outbound

This paper cites An evaluation of synthetic data generators implemented in the Python library synthcity.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models An evaluation of synthetic data generators implemented in the Python library synthcity

Reference 38

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Observation 37e1fbdd-3436-4aac-8f99-23ef7307fbdb · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data? In Advances in Neural Information Processing Systems , volume 35, pages 507–520, 2022.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Why do tree-based models still outperform deep learning on typical tabular data? In Advances in Neural Information Processing Systems , volume 35, pages 507–520, 2022

Reference 39

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Observation bfdb922b-8a0a-4ab5-b813-af63b10e7f1b · outbound

This paper cites Deep neural networks and tabular data: A survey.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Deep neural networks and tabular data: A survey

Reference 40

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Observation 3b7fbca8-b389-46f7-a0fe-de86da5b47bc · outbound

This paper cites Tabular data: Deep learning is not all you need.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Tabular data: Deep learning is not all you need

Reference 41

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Observation 7c49a041-b82a-4e82-8edb-2588e40ecb8a · outbound

This paper cites TabPFN: A transformer that solves small tabular classification problems in a second.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models TabPFN: A transformer that solves small tabular classification problems in a second

Reference 42

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Observation 2af6a68f-74de-481b-a8cf-525e1af853cd · outbound

This paper cites Mixed-type tabular data synthesis with score-based diffusion in latent space.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Mixed-type tabular data synthesis with score-based diffusion in latent space

Reference 43

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Observation efb4ca20-847f-4cc5-b277-a7ca0105c8ea · outbound

This paper cites A note on the evaluation of generative models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A note on the evaluation of generative models

Reference 44

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Observation 62f90e56-f935-4cf6-9d22-1689a5bc75d9 · outbound

This paper cites Assessing generative models via precision and recall.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Assessing generative models via precision and recall

Reference 45

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Observation 52512ce0-4066-4c61-9f33-fe2143628eb0 · outbound

This paper cites A practical guide to sample-based statistical distances for evaluating generative models in science.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A practical guide to sample-based statistical distances for evaluating generative models in science

Reference 46

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This paper cites Interpretable Machine Learning.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Interpretable Machine Learning

Reference 47

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This paper cites Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, and Bernd Bischl.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, and Bernd Bischl

Reference 48

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Observation 6a4e7ace-7cd8-41fa-815a-bdc974bede0f · outbound

This paper cites A unified approach to interpreting model predictions.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A unified approach to interpreting model predictions

Reference 49

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This paper cites Consistent Individualized Feature Attribution for Tree Ensembles.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Consistent Individualized Feature Attribution for Tree Ensembles

Reference 50

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Observation bf206a4c-17e4-4ded-9cc6-8ead428a4a5b · outbound

This paper cites Greedy function approximation: a gradient boosting machine.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Greedy function approximation: a gradient boosting machine

Reference 51

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Observation 608f3a29-9a6f-40d7-bf8f-5a96f6e355c7 · outbound

This paper cites Peeking inside the black box: Visualizing statisti- cal learning with plots of individual conditional expectation.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Peeking inside the black box: Visualizing statisti- cal learning with plots of individual conditional expectation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.470157Z

Source-reported events for the cited work

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Observation 4c7a1c03-ca2e-4ffd-b9fc-a92a15715568 · outbound

This paper cites All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.458637Z

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This paper cites Explaining individual predictions when features are dependent: More accurate approximations to Shapley values.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.446390Z

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Observation c58edd9c-ae2c-4054-907a-aef60aa9fcd7 · outbound

This paper cites Algorithms to estimate Shapley value feature attributions.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Algorithms to estimate Shapley value feature attributions

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.435266Z

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Observation 12feca0c-c5f1-4d3e-870d-d277978e5956 · outbound

This paper cites True to the Model or True to the Data?.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models True to the Model or True to the Data?

Reference 56

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Observation 1685f35b-abf5-4909-838f-fe84ec41dd62 · outbound

This paper cites Revisiting precision recall definition for generative modeling.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Revisiting precision recall definition for generative modeling

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.422343Z

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Observation aa0b3701-cce4-4b0f-b6b9-d95e8910766a · outbound

This paper cites XGBoost: A scalable tree boosting system.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models XGBoost: A scalable tree boosting system

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.408874Z

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Observation 3a58f6e9-2f0d-4d1e-bcd9-b827b5cd9670 · outbound

This paper cites Watson and Marvin N.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Watson and Marvin N

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.396452Z

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Observation d01a0bda-f265-4ebe-be98-4ee814948c77 · outbound

This paper cites A value for n-person games.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models A value for n-person games

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.383832Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 783215c4-1c75-4f7c-9b37-a9b31d78d331 · outbound

This paper cites Visualizing the effects of predictor variables in black box supervised learning models.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Visualizing the effects of predictor variables in black box supervised learning models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.372239Z

Source-reported events for the cited work

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Observation 90496230-d395-4d1c-8775-111a40e3384c · outbound

This paper cites The shapley taylor interaction index.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models The shapley taylor interaction index

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.359814Z

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Observation 2a358f7d-8a76-45ea-ae95-e5738414fec9 · outbound

This paper cites Counterfactual explanations and how to find them: literature review and benchmarking.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Counterfactual explanations and how to find them: literature review and benchmarking

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.347991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6789149-b943-4d9f-a6cf-2fb3653d9460 · outbound

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What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:26:12.336815Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd0e1a32-4f93-4deb-9b0f-9a1867907a6d · outbound

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What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:26:12.324322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f31cef2-ff7f-4b00-bf18-e78a89ce6e0a · outbound

This paper cites CTAB-GAN+: Enhancing tabular data synthesis.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models CTAB-GAN+: Enhancing tabular data synthesis

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.313056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad0855cd-64c2-4361-8e7c-983d8194bfdb · outbound

This paper cites Uci machine learning repository, 2017.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Uci machine learning repository, 2017

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:12.115256Z

Source-reported events for the cited work

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Observation 27cf8595-09a6-4a02-b805-b0cad8572673 · outbound

This paper cites Kaggle datasets repository.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Kaggle datasets repository

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.293291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ee5688f-97bb-41fe-bb4c-ad4e1716d819 · outbound

This paper cites Random forests.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Random forests

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:12.122601Z

Source-reported events for the cited work

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Observation cac07b83-314d-4450-a7c0-4c94e0816aa1 · outbound

This paper cites Unifying feature-based explanations with functional ANOV A and cooperative game theory.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Unifying feature-based explanations with functional ANOV A and cooperative game theory

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.274743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb78f5da-d001-4d2c-89d8-28bf1b00e007 · outbound

This paper cites Explaining predictive models with mixed features us- ing shapley values and conditional inference trees.

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Explaining predictive models with mixed features us- ing shapley values and conditional inference trees

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:12.262218Z

Source-reported events for the cited work

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