arXiv Statistics ML

Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data

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.

arXiv Machine Learning

Sep 11

Generalized Score Matching for Parameter Estimation on Convex Domains

The paper introduces a generalized score matching objective for parameter estimation on convex subsets of ρ^d, derived from Minimum Probability Flow learning. It shows that this objective is a proper local scoring rule of second order, ensuring recovery of the true density when minimized, and proves convexity and consistency for exponential family models under standard conditions. Experiments demonstrate the method’s effectiveness on constrained domains where the partition function is intractable, including a generative modeling use‑case.

By Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar Seelamantula

arXiv Machine Learning

Jun 4

Flow Matching Calibration for Simulation-Based Inference under Model Misspecification

arXiv:2509. 23385v5 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simulated data.

By Pierre-Louis Ruhlmann, Michael Arbel, Florence Forbes, Pedro L. C. Rodrigues

diffusion benchmarks

arXiv Machine Learning

Jun 2

Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

arXiv:2602. 19126v2 Announce Type: replace Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets.

By Michele Caprio, Katerina Papagiannouli, Siu Lun Chau, Sayan Mukherjee

arXiv Machine Learning

Jul 30

BayesAME: Bayesian Active Model Evaluation

arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.

By Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet, Virginia Aglietti, Silvia Chiappa

benchmarks

arXiv Machine Learning

Aug 10

Robust inference using density-powered Stein operators

arXiv:2511. 03963v3 Announce Type: replace-cross Abstract: We introduce a density-power weighted variant of the Stein operator, called the $\gamma$-Stein operator, for robust inference with unnormalized probability models.

By Shinto Eguchi

arXiv Machine Learning

Jun 11

CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference

arXiv:2508. 17077v3 Announce Type: replace-cross Abstract: Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods.

By Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos, Pedro L. C. Rodrigues, Rafael Izbicki

diffusion benchmarks

arXiv Machine Learning

Jun 5

Generalized TV--$\ell_p$ Structured Priors for Bayesian $T_1$ Mapping

arXiv:2606. 05381v1 Announce Type: new Abstract: We propose an extended family of structured spatial priors that incorporates the total variation (TV) function with $\ell_p$ norms.

By Disi Lin, Martin Berggren, Tommy L\"ofstedt

rag safety

arXiv Machine Learning

Aug 7

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

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.

By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi

benchmarks safety

arXiv Statistics ML

Aug 25

Guidance for Prior Change via Density Ratio Estimation

arXiv:2608.21729v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likeliho...

By Yichen Zang, Song Liu, Jiun-Yi Lin

safety

arXiv Machine Learning

Jun 26

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.

By Graham Gibson, John Tipton, Kellin Rumsey, Natalie Klein

benchmarks

arXiv Machine Learning

Sep 1

Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models

arXiv:2608.29349v1 Announce Type: new Abstract: Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-e...

By Yean Hoon Ong, Paolo Barucca, Wei Pan, Jun Wang

arXiv Machine Learning

Jun 2

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

arXiv:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.

By Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel, Rahul G. Krishnan

llms safety

Related stories