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Found 8 papers, 2 papers with code
On Regularization and Inference with Label Constraints
Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems.
Are you using test log-likelihood correctly?
Test log-likelihood is commonly used to compare different models of the same data or different approximate inference algorithms for fitting the same probabilistic model.
Many processors, little time: MCMC for partitions via optimal transport couplings
In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias.
Measuring the robustness of Gaussian processes to kernel choice
We demonstrate in both synthetic and real-world examples that decisions made with a GP can exhibit non-robustness to kernel choice, even when prior draws are qualitatively interchangeable to a user.
Independent versus truncated finite approximations for Bayesian nonparametric inference
Bayesian nonparametric models based on completely random measures (CRMs) offers flexibility when the number of clusters or latent components in a data set is unknown.
Independent finite approximations for Bayesian nonparametric inference
We call our construction the automated independent finite approximation (AIFA).
Approximate Cross-Validation for Structured Models
But this existing ACV work is restricted to simpler models by the assumptions that (i) data across CV folds are independent and (ii) an exact initial model fit is available.
PAC-Bayes Tree: weighted subtrees with guarantees
We present a weighted-majority classification approach over subtrees of a fixed tree, which provably achieves excess-risk of the same order as the best tree-pruning.
