Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
Joint distribution optimal transportation for domain adaptation.Advances in neural information processing systems, 30
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Conditional optimal transport is used to turn raw PRM outputs into monotonic quantile functions that improve calibration and downstream Best-of-N performance on MATH-500 and AIME.
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Fused Gromov-Wasserstein Distance with Feature Selection
Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
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Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
Conditional optimal transport is used to turn raw PRM outputs into monotonic quantile functions that improve calibration and downstream Best-of-N performance on MATH-500 and AIME.