6 citations · 9 across the 5 of their papers we have counts for
5 papers
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Gitte Kremling, Jeffrey Näf, Johannes Lederer
The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled nonparametric methods to deal with gener…
Non-asymptotic error bounds for probability flow ODEs under weak log-concavity
Gitte Kremling, Francesco Iafrate, Mahsa Taheri +1
Score-based generative modeling, implemented through probability flow ODEs, has shown impressive results in numerous practical settings. However, most convergence guarantees rely o…
Asymptotic properties of the MLE in distributional regression under random censoring
Gitte Kremling, Gerhard Dikta
Distributional regression aims to find the best candidate in a given parametric family of conditional distributions to model a given dataset. As each candidate in the distribution…
Bootstrap-Based Goodness-of-Fit Test for Parametric Families of Conditional Distributions
Gitte Kremling, Gerhard Dikta
A consistent goodness-of-fit test for distributional regression is introduced. The test statistic is based on a process that traces the difference between a nonparametric and a sem…
Convergence analysis of multi-level spectral deferred corrections
Gitte Kremling, Robert Speck
The spectral deferred correction (SDC) method is class of iterative solvers for ordinary differential equations (ODEs). It can be interpreted as a preconditioned Picard iteration f…