Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
Representation Learning: A Review and New Perspectives , year=
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An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
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Content-Style Identification via Differential Independence
Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
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Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
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Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.