arXiv Machine Learning June 17, 2026 By Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani

A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer

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arXiv:2606. 17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors.

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efficiency

arXiv Machine Learning

Jul 28

Amortized Bayesian Causal Discovery of Extended Factor Graphs

arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.

By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch

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arXiv Machine Learning

Jun 9

Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference

arXiv:2606. 07677v1 Announce Type: cross Abstract: Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors.

By Shengxian Ding, Haonan Gao, Pangpang Liu, Xinyuan Tian, Yize Zhao

arXiv Machine Learning

Jul 16

Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

arXiv:2607. 13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrating these data sources within a single population modeling framework remains challenging.

By Anders Sj\"oberg, Nils Olsson, Marcus Baaz, Mats Jirstrand

multimodal

arXiv Machine Learning

Aug 5

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.

By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen

rag multimodal benchmarks

arXiv AI

Jun 12

Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation

arXiv:2606. 13556v1 Announce Type: new Abstract: Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of individual behavioral data before they can distinguish constitutional variation from environmentally driven deviation.

By Aruna Dey, Suraj Biswas

arXiv Machine Learning

Jul 20

Deep and Probabilistic Models for Gene Regulatory Network Inference

arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.

By Claudia Skok Gibbs

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