arXiv Machine Learning

Environment-Robust Representation Learning with Empirical Bayes

arXiv:2606. 05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems.

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

Jun 24

Predictive variational inference: Learn the predictively optimal posterior distribution

arXiv:2410. 14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model misspecification.

By Jinlin Lai, Antonio Linero, Yuling Yao

arXiv Machine Learning

Jun 19

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction

arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.

By Shuozhi Zuo, Yixin Wang

arXiv Machine Learning

Jun 18

Anti-causal domain generalization: Leveraging unlabeled data

arXiv:2602. 17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments.

By Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml

arXiv Machine Learning

Jul 7

Bayesian Invariance Modeling of Multi-Environment Data

arXiv:2506. 22675v4 Announce Type: replace-cross Abstract: Invariant prediction [Peters et al.

By Luhuan Wu, Mingzhang Yin, Yixin Wang, John P. Cunningham, David M. Blei

arXiv Machine Learning

Aug 12

Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift

arXiv:2505. 02257v2 Announce Type: replace-cross Abstract: In regions lacking medically certified causes of death, verbal autopsy (VA) is a widely used tool to ascertain the cause of death through interviews with caregivers.

By Yu Zhu, Jason Teng, Zehang Richard Li

safety

arXiv AI

Jun 9

Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring

arXiv:2606. 09433v1 Announce Type: new Abstract: Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden.

By Yixuan Zhang (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark), Yang Song (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark), Hao Wang (Rutgers University, New Brunswick, NJ, USA), Samir Bhatt (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark, MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom), Hengguan Huang (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark, MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom)

benchmarks

arXiv Machine Learning

Jun 19

Variational Consensus Monte Carlo for Bayesian Mixture

arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.

By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk

benchmarks safety

arXiv Machine Learning

Jun 16

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.

By Yuda Shao, Zhiling Gu, Shan Yu

computer-vision

arXiv AI

Jun 26

Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC

arXiv:2606. 27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making.

By Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker, Carolina J. Klett-Tammen, Torben Heinsohn, Wolfgang Wiechert, Katharina Noeh, Stefan Kesselheim

arXiv Machine Learning

Jun 9

Hierarchical Projection for Adaptive Knowledge Transfer

arXiv:2606. 08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains.

By Samhita Pal, Tian Gu

fine-tuning safety

arXiv AI

Jun 19

Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions

arXiv:2606. 20459v1 Announce Type: new Abstract: IVF pregnancy rates are routinely modeled using patient-level variables, while high-resolution laboratory environmental data remain underutilized.

By Zahra Asghari Varzaneh, Reza Khoshkangini, Pia Saldeen, Lars Johansson, Thomas Ebner

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