TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
Machine learning for synthetic data generation: a review
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A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
Derives matched converse and achievability bounds that characterize optimal trade-offs among false-alarm probability, detection error probability, distortion, and information rate for multi-bit watermarking of stationary ergodic stochastic processes.
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Self-Improving Tabular Language Models via Iterative Group Alignment
TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
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Generative AI-Based Monte Carlo Simulation for Method Evaluation Using Synthetic Multilevel Data
A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
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Fundamental Trade-Offs in Multi-Bit Watermarking of Stochastic Processes
Derives matched converse and achievability bounds that characterize optimal trade-offs among false-alarm probability, detection error probability, distortion, and information rate for multi-bit watermarking of stationary ergodic stochastic processes.