arXiv Machine Learning
Jun 5
Environment-Robust Representation Learning with Empirical Bayes
arXiv:2606. 05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems.
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.
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arXiv Machine Learning
Jul 2
Generative Model Proposal based Particle Filtering for Data Assimilation
arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.
arXiv Machine Learning
Aug 10
EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting
arXiv:2608. 06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks.
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.
arXiv Machine Learning
Jul 15
LatentFlow: A General Framework for Conditioning Stochastic Processes
arXiv:2607. 12922v1 Announce Type: cross Abstract: Stochastic-process models are, as a rule, far easier to simulate than to condition.
Hugging Face Trending Papers
Jul 14
LatentFlow: A General Framework for Conditioning Stochastic Processes
Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints all induce intractable conditional laws, requiring bespoke, model-specific constructions.
arXiv AI
Jul 28
Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery
arXiv:2607. 23896v1 Announce Type: new Abstract: Autonomous laboratories automate experimental execution, but a campaign must also decide which recovery pathway merits optimization.
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.
arXiv AI
Jun 16
Bayesian Inference and Decision Audits for Public Archives of Frontier AI Evaluations
arXiv:2606. 17005v1 Announce Type: new Abstract: Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules, benchmark revisions, and missingness.
arXiv Machine Learning
Jun 25
A Probabilistic Framework for LLM-Based Model Discovery
arXiv:2602. 18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress.
arXiv Machine Learning
Jun 4
Bayesian learning for the stochastic shortest path problem
arXiv:2606. 04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP).
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.