arXiv:2606. 05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems.
By Yuli Slavutsky, Matthew Shen, Bohan Wu, David M. Blei
This paper introduces a hierarchical Bayesian multitask learning model that assumes a shared sparsity structure across different binary classification tasks. The authors develop a variational inference algorithm for efficient posterior approximation and evaluate the method on synthetic data and pooled microbiome studies. Results show superior support recovery in synthetic experiments and robust, well‑calibrated predictions with informative taxa selection in microbiome classification.
By Haonan Zhu, Andre R. Goncalves, Camilo Valdes, Hiranmayi Ranganathan, Boya Zhang, Jose Manuel Mart\'i, Car Reen Kok, Monica K. Borucki, Nisha J. Mulakken, James B. Thissen, Crystal Jaing, Alfred Hero, Nicholas A. Be
arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.
By Chandni Nagda, Mayank Shrivastavam Gudrun Thorkelsdottir, Gan Zhang, Morteza Mardani, Arindam Banerjee
arXiv:2608. 06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks.
By Aniruddha Adiga, Jingyuan Chou, Gursharn Kaur, Andrew Warren, Srinivasan Venkatramanan, Baltazar Espinoza, Bryan Lewis, Justin Crow, Alexandra Lorentz, Rekha Singh, Madhav Marathe
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:2607. 12922v1 Announce Type: cross Abstract: Stochastic-process models are, as a rule, far easier to simulate than to condition.
By Louis Sharrock, Lachlan Astfalck, Henry Moss