arXiv Statistics ML

Guidance for Prior Change via Density Ratio Estimation

arXiv Machine Learning

Jun 10

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.

By Ludvig Doeser, Jens Jasche

arXiv Machine Learning

Jun 19

Multi-Task Bayesian In-Context Learning

arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.

By Qingyang Zhu, Eric Karl Oermann, Kyunghyun Cho

llms benchmarks

arXiv Machine Learning

1d ago

Source Distribution Estimation by Posterior Averaging

arXiv:2609.02622v1 Announce Type: new Abstract: Simulation-based science often requires a distribution over simulator parameters whose push-forward reproduces a set of real observations: this is the...

By Trung-Dung Hoang, Lisa M. Koch

benchmarks

arXiv Machine Learning

Jul 9

Bifidelity Parameter Estimation Using Conditional Diffusion Models

arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.

By Caroline Tatsuoka, Minglei Yang, Dongbin Xiu, Guannan Zhang

diffusion multimodal

arXiv AI

Jun 2

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502. 04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more.

By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana

diffusion benchmarks

arXiv Machine Learning

Jun 25

An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes

arXiv:2606. 25882v1 Announce Type: new Abstract: DGPs are probabilistic models with remarkable prediction performance that concatenate GPs across several layers.

By Francisco Javier S\'aez-Maldonado, Juan Maro\~nas, Daniel Hern\'andez-Lobato

arXiv AI

Aug 17

FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC

arXiv:2608. 13774v1 Announce Type: new Abstract: Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models.

By Harini Venkatesan, Christian Shelton, Ming-Feng Ho, Simeon Bird, Mengxuan Wu

arXiv Machine Learning

Jul 16

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.

By Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck

benchmarks

arXiv AI

Jul 2

Calibrated Test-Time Guidance for Bayesian Inference

arXiv:2602. 22428v2 Announce Type: replace-cross Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function.

By Daniel Geyfman, Felix Draxler, Jan Groeneveld, Hyunsoo Lee, Theofanis Karaletsos, Stephan Mandt

diffusion benchmarks

arXiv Machine Learning

Jun 2

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.

By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal

benchmarks

arXiv Machine Learning

Jun 26

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510. 17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved.

By Bo Liang, Hanlin Song, Chang Liu, Tianyu Zhao, Yuxiang Xu, Zihao Xiao, Manjia Liang, Minghui Du, Wei-Liang Qian, Li-e Qiang, Peng Xu, Ziren Luo

diffusion

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

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