Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes | Paper Citation Record

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This paper cites Google dataset search by the numbers,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Google dataset search by the numbers,

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This paper cites Statistical relational tables for statistical database manage- ment,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Statistical relational tables for statistical database manage- ment,

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This paper cites A multi-task learning framework for reading comprehension of scientific tabular data,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A multi-task learning framework for reading comprehension of scientific tabular data,

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This paper cites S i 1 o f use: Cross- silo synthetic data generation with latent tabular diffusion models,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes S i 1 o f use: Cross- silo synthetic data generation with latent tabular diffusion models,

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This paper cites Challenges and opportunities of generative models on tabular data,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Challenges and opportunities of generative models on tabular data,

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This paper cites Kovalerchuk and E.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Kovalerchuk and E

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This paper cites Differential Privacy and Machine Learning: a Survey and Review.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Differential Privacy and Machine Learning: a Survey and Review

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This paper cites Syn- thetic data generation for tabular health records: A systematic review,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Syn- thetic data generation for tabular health records: A systematic review,

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This paper cites Modeling tabular data using conditional gan,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Modeling tabular data using conditional gan,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Tabddpm: Modelling tabular data with diffusion models,

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

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Mixed-type tabular data synthesis with score-based diffusion in latent space,

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This paper cites Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey

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This paper cites Language mod- els are few-shot learners,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Language mod- els are few-shot learners,

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This paper cites Large lan- guage models are zero-shot reasoners,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Large lan- guage models are zero-shot reasoners,

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This paper cites Language models are realistic tabular data generators,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Language models are realistic tabular data generators,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Fraud detection using machine learning and deep learning,

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This paper cites Toward a unified framework for unsupervised complex tabular reasoning,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Toward a unified framework for unsupervised complex tabular reasoning,

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This paper cites A review on healthcare data privacy and security,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A review on healthcare data privacy and security,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Metadiff: Meta-learning with conditional diffusion for few-shot learning,

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This paper cites Epic: Effective prompting for imbalanced- class data synthesis in tabular data classification via large language models,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Epic: Effective prompting for imbalanced- class data synthesis in tabular data classification via large language models,

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This paper cites Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research

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This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

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This paper cites How realistic is your synthetic data? constraining deep generative models for tabular data,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes How realistic is your synthetic data? constraining deep generative models for tabular data,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Prompt Design and Engineering: Introduction and Advanced Methods

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Frequency balanced datasets lead to better language models,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Harmonic: Harnessing llms for tabular data synthesis and privacy protection,

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This paper cites Large language models (llms) on tabular data: Prediction, generation, and understanding - a survey,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Large language models (llms) on tabular data: Prediction, generation, and understanding - a survey,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Why do tree-based models still outperform deep learning on typical tabular data?

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Tabular data: Deep learning is not all you need,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Hierarchical pruning of deep ensembles with focal diversity,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Pruning of random forest classifiers: A survey and future directions,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A novel ensemble learning method using majority based voting of multiple selective decision trees,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Self-consistency improves chain of thought reasoning in language models,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Robust learning meets generative models: Can proxy dis- tributions improve adversarial robustness?

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A survey on llm-generated text detection: Necessity, methods, and future directions,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Privacy mechanisms and evaluation metrics for synthetic data generation: A systematic review,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Synthetic Data Privacy Metrics

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Gintropy: Gini index based generalization of entropy,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A guide to formulating fairness in an optimization model,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes A systematic review of synthetic data generation techniques using generative ai,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Benchmark Data Contamination of Large Language Models: A Survey

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Detection of large language model contamination with tabular data,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Elephants never forget: Memorization and learning of tabular data in large language models,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Xgboost: A scalable tree boosting system,

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Faithful Logical Reasoning via Symbolic Chain-of-Thought

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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Chain-of-thought prompting elicits reasoning in large language models,

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This paper cites Decomposition of gini and the generalized entropy inequality measures,.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Decomposition of gini and the generalized entropy inequality measures,

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