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https://openreview.net/forum?id=xgTV6rmH6n Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks | OpenReview Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue... prior data fitted efficient bayesian learning curve networks openreview extrapolation https://openreview.net/forum?id=VQpqxucNX63 Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks | OpenReview We show that Prior-data Fitted Networks (PFNs) compare favorably against MCMC for learning curve inference. prior data fitted efficient bayesian learning curve networks openreview extrapolation https://www.sandia.gov/research/publications/details/bayesian-learning-with-gaussian-processes-for-low-dimensional-representatio-2025-05-01/ Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent... bayesian learning gaussian processes low dimensional time dependent representations https://openreview.net/forum?id=dX9MjUtP1A Self-Correcting Bayesian Optimization through Bayesian Active Learning | OpenReview Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to... self correcting bayesian optimization active learning openreview https://lucapresicce.r-universe.dev/spBPS spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning transfer learning bayesian predictive stacking scalable https://www.chemeurope.com/en/whitepapers/1127054/data-driven-reaction-optimization-in-process-chemistry.html Data-Driven Reaction Optimization in Process Chemistry - Bayesian Optimization, Transfer Learning,... An intuitive platform making advanced data-driven optimization available to wider chemistry teams. data driven transfer learning reaction optimization process https://www.intechopen.com:443/chapters/58908 Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning... Machine-learning (ML) methods are finding increasing application to guide human decision-making in many fields. Such guidance can have important consequences,... machine learning preventing disparities bayesian frequentist