arXiv Machine Learning
Data Fusion for Errors-in-Variables
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Statistics ML
Aug 25
Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
arXiv Machine Learning
Jul 2
Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
arXiv:2606. 25169v2 Announce Type: replace-cross Abstract: Sampling from an unnormalized target by reversing an Ornstein-Uhlenbeck diffusion requires the score of each noise-perturbed marginal.
arXiv Statistics ML
Sep 4
Distributional Treatment Effect Transportability across Heterogeneous Sites
The paper investigates how to transport the full distribution of treatment effects from a source site to a target site in a cross‑site, one‑armed design where only control units are observed in the target. It proposes learning a transformation between sites via optimal transport on control samples, then applying this transformation to the source treated sample to generate a synthetic target treated distribution. The authors prove convergence of this synthetic distribution to the true target distribution and validate the method with simulations and a patient‑derived xenograft data application.
arXiv Machine Learning
Aug 4
Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.
arXiv Machine Learning
Aug 4
Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models
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 Machine Learning
Jun 25
Laplace--Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
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.