MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
Springer, 2006
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Variational predictive resampling iteratively imputes data from a variational predictive to produce posterior samples that converge to the exact Bayesian posterior in Gaussian models where mean-field VI retains a gap.
Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
Neural networks regress oversized subspaces for parametric problems using subspace-specific losses, with theory and experiments showing improved accuracy and smoother mappings.
Soft-DPG uses Gaussian smoothing on the Bellman equation to derive a well-defined policy gradient without relying on critic action derivatives, yielding competitive performance on dense-reward tasks and gains on discretized-reward variants.
Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.
citing papers explorer
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Training-Free Generative Sampling via Moment-Matched Score Smoothing
MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
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Variational predictive resampling
Variational predictive resampling iteratively imputes data from a variational predictive to produce posterior samples that converge to the exact Bayesian posterior in Gaussian models where mean-field VI retains a gap.
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Decision-Aware Predictions for Right-Hand Side Parameters in Linear Programs
Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
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Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
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Deep Learning for Subspace Regression
Neural networks regress oversized subspaces for parametric problems using subspace-specific losses, with theory and experiments showing improved accuracy and smoother mappings.
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Soft Deterministic Policy Gradient with Gaussian Smoothing
Soft-DPG uses Gaussian smoothing on the Bellman equation to derive a well-defined policy gradient without relying on critic action derivatives, yielding competitive performance on dense-reward tasks and gains on discretized-reward variants.
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A Comparative Study of UMAP and Other Dimensionality Reduction Methods
Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.