TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer | Paper Citation Record

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This paper cites Optuna: A next- generation hyperparameter optimization framework.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Optuna: A next- generation hyperparameter optimization framework

Reference 1

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This paper cites Arik and Tomas Pfister.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Arik and Tomas Pfister

Reference 2

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This paper cites Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E

Reference 3

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 4

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This paper cites Language models are realistic tabular data generators.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Language models are realistic tabular data generators

Reference 5

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This paper cites Breiman, Jerome H.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Breiman, Jerome H

Reference 6

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This paper cites Random forests.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Random forests

Reference 7

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This paper cites Rank consistent ordinal regression for neural networks with application to age estimation.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Rank consistent ordinal regression for neural networks with application to age estimation

Reference 8

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This paper cites A simple log-based loss function for ordinal text classification.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer A simple log-based loss function for ordinal text classification

Reference 9

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This paper cites XGBoost: A scalable tree boosting system.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer XGBoost: A scalable tree boosting system

Reference 10

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This paper cites Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov

Reference 11

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This paper cites Big data in healthcare: management, analysis and future prospects.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Big data in healthcare: management, analysis and future prospects

Reference 12

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This paper cites DataCebo, Inc., 12 2023.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer DataCebo, Inc., 12 2023

Reference 13

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This paper cites Soft labels for ordinal regression.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Soft labels for ordinal regression

Reference 14

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 16

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 17

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This paper cites Generative adversarial nets.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Generative adversarial nets

Reference 18

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This paper cites Revisiting deep learning models for tabular data.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Revisiting deep learning models for tabular data

Reference 19

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This paper cites On embeddings for numerical features in tabular deep learning.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer On embeddings for numerical features in tabular deep learning

Reference 20

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This paper cites Why do tree-based models still outperform deep learning on typical tabular data? In S.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Why do tree-based models still outperform deep learning on typical tabular data? In S

Reference 21

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This paper cites TabMT: Generating tabular data with masked transformers.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer TabMT: Generating tabular data with masked transformers

Reference 22

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This paper cites Hedonic housing prices and the demand for clean air.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Hedonic housing prices and the demand for clean air

Reference 23

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This paper cites Synthetic data generation for tabular health records: A systematic review.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Synthetic data generation for tabular health records: A systematic review

Reference 24

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This paper cites Denoising diffusion probabilistic models.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Denoising diffusion probabilistic models

Reference 25

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This paper cites Statlog (German Credit Data).

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Statlog (German Credit Data)

Reference 26

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

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer TabPFN: A transformer that solves small tabular classification problems in a second

Reference 27

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This paper cites Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks

Reference 28

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This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 29

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This paper cites Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees

Reference 30

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer LightGBM: A highly efficient gradient boosting decision tree

Reference 31

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Kingma and Max Welling

Reference 32

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid

Reference 33

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Learning probabilistic ordinal embeddings for uncertainty-aware regression

Reference 34

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer MacQueen

Reference 35

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 36

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Quantitative structure–activity relationship models for ready biodegradability of chemicals

Reference 37

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unmasking Trees for Tabular Data

Reference 38

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This paper cites Using data mining for bank direct marketing: An application of the CRISP-DM methodology.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Using data mining for bank direct marketing: An application of the CRISP-DM methodology

Reference 39

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This paper cites SLACE: A monotone and balance-sensitive loss function for ordinal regression.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer SLACE: A monotone and balance-sensitive loss function for ordinal regression

Reference 40

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This paper cites Cat2Vec with position encoding: A new approach for handling ordinal features using learned embeddings with positional encoding.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Cat2Vec with position encoding: A new approach for handling ordinal features using learned embeddings with positional encoding

Reference 41

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 42

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This paper cites Ordinal regression with multiple output CNN for age estimation.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Ordinal regression with multiple output CNN for age estimation

Reference 43

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Data synthesis based on generative adversarial networks

Reference 44

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Proposed guide on synthetic data generation

Reference 45

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer CatBoost: unbiased boosting with categorical features

Reference 46

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Synthcity: a benchmark framework for diverse use cases of tabular synthetic data

Reference 47

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Language models are unsupervised multitask learners

Reference 48

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Hamprecht, Yoshua Bengio, and Aaron C

Reference 49

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 50

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer TabDiff: a mixed-type diffusion model for tabular data generation

Reference 51

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This paper cites Deep neural networks for rank-consistent ordinal regression based on conditional probabilities.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Deep neural networks for rank-consistent ordinal regression based on conditional probabilities

Reference 52

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

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Tabular data: Deep learning is not all you need

Reference 53

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This paper cites Everhart, W.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Everhart, W

Reference 54

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 55

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Nick Street, W

Reference 56

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer van Rijn, Bernd Bischl, and Luis Torgo

Reference 57

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This paper cites Attention is all you need.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Attention is all you need

Reference 58

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Observation b55132c3-21e0-47c0-8de3-cc3458ce2fd0 · outbound

This paper cites Wang and Binh P.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Wang and Binh P

Reference 59

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Observation c85456ed-7d55-4de0-8354-b26b577f4ec5 · outbound

This paper cites HARMONIC: Harnessing llms for tabular data synthesis and privacy protection.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer HARMONIC: Harnessing llms for tabular data synthesis and privacy protection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.128848Z

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Observation a5dbd9ad-aad5-4ed6-b991-6647bf24838c · outbound

This paper cites Watson, Kristin Blesch, Jan Kapar, and Marvin N.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Watson, Kristin Blesch, Jan Kapar, and Marvin N

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.116536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 90f02e32-1258-4374-840f-a7eed7f636aa · outbound

This paper cites Multisurface method of pattern separation for medical diagnosis applied to breast cytology.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Multisurface method of pattern separation for medical diagnosis applied to breast cytology

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.104798Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5936a260-1da5-4d88-9776-95f9d8803502 · outbound

This paper cites CvT: Introducing convolutions to vision transformers.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer CvT: Introducing convolutions to vision transformers

Reference 63

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Observation 7d8b5065-bf48-4b3d-bbd8-e1ed2bee140c · outbound

This paper cites Modeling tabular data using conditional GAN.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Modeling tabular data using conditional GAN

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.093981Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20038410-d4b1-459f-beab-9dc5b5a3ae6f · outbound

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

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Mixed-type tabular data synthesis with score-based diffusion in latent space

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.082365Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8cff9e0-1d25-4fd9-b026-bb7a6e4fe8dc · outbound

This paper cites Improving deep regression with ordinal entropy.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Improving deep regression with ordinal entropy

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.071579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0ca1c62-96d3-46ba-8217-df2376d692b2 · outbound

This paper cites an unresolved cited work.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:36:53.060006Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9fbf1118-6242-4b9b-bd62-8dc8edc3e681 · outbound

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

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer TabuLa: Harnessing Language Models for Tabular Data Synthesis

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T22:36:51.848241Z

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Observation 61d4ade5-ad02-4057-8217-f8b3f60db484 · outbound

This paper cites kmeans” and “quantile.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer kmeans” and “quantile

Reference 69

Resolution: malformed identifier; no resolver link, observed 2026-08-10T22:36:51.852484Z

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Observation 2e2c81ee-b74f-46c7-8759-32d8242adc0e · outbound

This paper cites Detailed theoretical proofs are provided in the Appendix.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Detailed theoretical proofs are provided in the Appendix

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.049192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44d9c3dd-f32b-48ac-8575-c8a80b66c120 · outbound

This paper cites Limitations.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Limitations

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.037722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6be86b54-0f96-405a-a223-fe5029bcabb6 · outbound

This paper cites Except the Theorem 1 which is easy to prove, we provide detailed theoretical proofs for the rest of Theorems and Lemma in Appendix B.5, Appendix C.1 and Appendix C.2.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Except the Theorem 1 which is easy to prove, we provide detailed theoretical proofs for the rest of Theorems and Lemma in Appendix B.5, Appendix C.1 and Appendix C.2

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.025705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24d706cd-7c94-4ac8-98f0-ec809dabf5d3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the paper does not include experiments

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.014204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7580a84f-5660-49cf-aad9-34d6fd1fad6c · outbound

This paper cites All datasets are sourced from OpenML; detailed loading instructions are provided in Appendix D.2.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer All datasets are sourced from OpenML; detailed loading instructions are provided in Appendix D.2

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:53.002723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4402bada-c154-409d-8364-7e300369e089 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the paper does not include experiments

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.991354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a85f728-4a25-4d18-8756-333e2ba9b447 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the paper does not include experiments

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.979956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe823690-44d6-40a2-bc52-48e332dbeb5f · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the paper does not include experiments

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.968277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f142940b-7244-4c07-a563-08fee0e76517 · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.956233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86a7ddc0-6d9e-444a-97f0-b7099c2c0e2e · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.943932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8bcb32f-1266-4bac-a6f7-1b1ac891f417 · outbound

This paper cites an unresolved cited work.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:36:52.932459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2f18a8df-eea7-4921-866b-e3db792b5df9 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Guidelines: • The answer NA means that the paper does not use existing assets

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.921031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c602891-571a-4804-8abe-1d4eba812324 · outbound

This paper cites • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:52.909199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 960ccfd2-6d6e-46dc-b892-64390f08f980 · outbound

This paper cites an unresolved cited work.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:36:52.897399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2e0c542d-d333-417b-ad74-164bf12c8f17 · outbound

This paper cites • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-10T22:36:51.913626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98657397-1148-4bd8-8b3e-b5d67d08ba0c · outbound

This paper cites an unresolved cited work.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:36:52.877535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 68dcacc8-665e-4dde-bb43-65ec6dfb68b2 · outbound

This paper cites ISBN 1581138385.

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer ISBN 1581138385

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-10T22:36:51.740814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56030f37-ab7e-4b54-8106-abfdac9c05d6 · outbound

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TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer doi: 10.14778/3231751.3231757

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T22:36:51.752583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers