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Found 7 papers, 3 papers with code
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
In the present paper, we show that, in the case of ridge regression, the CV loss may fail to be quasiconvex and thus may have multiple local optima.
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.
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Our second key insight is that, in the presence of ALR data, error in existing ACV methods roughly grows with the (approximate, low) rank rather than with the (full, high) dimension.
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.
Approximate Cross-Validation in High Dimensions with Guarantees
Crucially, though, we are able to show, both empirically and theoretically, that one approximation can perform well in high dimensions -- in cases where the high-dimensional parameter exhibits sparsity.
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
Bayesian nonparametric hidden Markov models are typically learned via fixed truncations of the infinite state space or local Monte Carlo proposals that make small changes to the state space.
