The paper introduces a method that uses confidence sequences to adaptively determine the number of Monte Carlo samples needed for Bayesian neural network predictions, ensuring statistical guarantees. It applies to tasks such as class identification, full predictive distribution approximation, and probability-threshold decisions, stopping sampling once the desired confidence is achieved. Experiments demonstrate efficient computational budgeting, allocating more samples to ambiguous inputs, preserving decision reliability, and reducing overall latency compared to a fixed sample budget.
By Fabian Denoodt, Sibylle Hess
arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.
By Xu Zhang, Wenpeng Li, Yunfeng Shao, Yonglin Liu, Kaiwen Zhou, Yinchuan Li
PACE introduces a propagation‑aware collaborative correction for one‑shot personalized federated graph learning. Each client sends a rank‑r update and a diagonal sketch of message moments, allowing the server to construct a correction that anchors to the receiver’s local model. A convex negative‑log‑likelihood calibration selects a single coefficient to blend local and external logits, improving accuracy and weighted‑F1 on most datasets while preserving local predictions when the correction is unhelpful.
By Ruizhe Huang, Chengran Li, Xiaochuan Shi
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
arXiv:2507. 21449v2 Announce Type: replace-cross Abstract: Degeneracy is an inherent feature of the loss landscape of neural networks, but it is not well understood how stochastic gradient MCMC (SGMCMC) algorithms interact with this degeneracy.
By Rohan Hitchcock, Jesse Hoogland
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
arXiv:2609.39525v1 Announce Type: new
Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
By Hans Olischl\"ager, Svenja Jedhoff, \v{S}imon Kucharsk\'y, Aayush Mishra, Stefan T. Radev, Paul B\"urkner
arXiv:2605. 08446v3 Announce Type: replace Abstract: Bayesian neural networks are typically trained against the evidence lower bound (ELBO), whose Jensen gap closes only when the variational posterior is exact.
By Pavel Prochazka
The paper investigates how well Prior-Data Fitted Networks (PFNs) approximate Bayesian posterior predictive distributions (PPDs). By decomposing deployment error into prior, context, and amortization components, the authors find that PFNs generally recover the target PPD accurately, but residual amortization errors remain in rare settings, leading to disagreement among independently pretrained models. They demonstrate that Bayesian model averaging can consistently reduce this uncertainty and improve predictions, even matching full-size TabICLv2 models.
By Manuel H\"ulskamp, Julius Kobialka, Emanuel Sommer, Thomas Nagler, David R\"ugamer
Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...
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
arXiv:2606. 14235v1 Announce Type: new Abstract: Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions.
By Jian Xu, Shigui Li, Wei Chen, Jiacheng Li, Zhiqi Lin, Delu Zeng, Xinghao Ding, John Paisley, Qibin Zhao