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Found 5 papers, 4 papers with code
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs.
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query.
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension.
Simplifying Neural Network Training Under Class Imbalance
Real-world datasets are often highly class-imbalanced, which can adversely impact the performance of deep learning models.
Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC
NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension.
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