arXiv:2608. 01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations.
arXiv:2609.12953v1 Announce Type: new Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-de...
arXiv:2607. 27933v3 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
arXiv:2607. 27933v2 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
arXiv:2607. 05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability.
Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs.
Conformal Calibration Transfer addresses the challenge of applying conformal prediction when labeled calibration data is only available in a source space, while predictions are needed in a target space linked via unlabeled paired observations. The proposed Transported Conformal Calibration (TCC) method transports source calibration into the target domain and then corrects residual mismatches using only unlabeled target inputs, with two variants: TCC‑KS, which conservatively adjusts calibration based on a label‑free uncertainty surrogate, and weighted‑TCC, which reweights transported calibration for efficiency when weights are stable. Finite‑sample target‑domain coverage guarantees are provided, and experiments on CIFAR‑100‑C, Tiny‑ImageNet‑C, and SEN12MS demonstrate reliable coverage transfer without labeled target data, along with label‑free diagnostics to signal when correction is required.
arXiv:2606. 30230v1 Announce Type: cross Abstract: Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training.
arXiv:2607. 21721v1 Announce Type: cross Abstract: Learned generative priors are increasingly used for ill-posed Bayesian inverse problems, their posterior uncertainty treated as earned from data.
arXiv:2606. 25169v1 Announce Type: cross Abstract: Sampling from an unnormalized target by reversing an Ornstein--Uhlenbeck diffusion requires the score of each noise-perturbed marginal.
arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.
The paper introduces SME-BETEL, a semiparametric Bayesian method that merges score matching estimating equations with Bayesian exponentially tilted empirical likelihood to perform inference on models with intractable normalizing constants. SME-BETEL avoids evaluating these constants and eliminates the need for learning-rate calibration, while providing consistency, asymptotic normality, and a Bernstein‑von Mises theorem that guarantees asymptotically calibrated credible sets even under model misspecification. The authors extend the framework to mixed‑domain data, enabling robust inference for doubly‑intractable models such as spatial preferential sampling, and demonstrate its effectiveness through simulations and an ozone‑monitoring application.