36 citations · 78 across the 14 of their papers we have counts for
16 papers
Exact Graph Learning via Integer Programming
Lucas Kook, Søren Wengel Mogensen
Learning the dependence structure among variables in complex systems is a central problem across medical, natural, and social sciences. These structures can be naturally represente…
Comparison of generalised additive models and neural networks in applications: A systematic review
Jessica Doohan, Lucas Kook, Kevin Burke
Neural networks have become a popular tool in predictive modelling, more commonly associated with machine learning and artificial intelligence than with statistics. Generalised Add…
Rethinking player evaluation in sports: Goals above expectation and beyond
Robert Bajons, Lucas Kook
A popular quantitative approach to evaluating player performance in sports involves comparing an observed outcome to the expected outcome ignoring player involvement, which is esti…
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber +2
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases. These biases present immediate risks in the algorithms' application. It…
Instrumental Variable Estimation of Distributional Causal Effects
Lucas Kook, Niklas Pfister
Estimating the causal effect of a treatment on the entire response distribution is an important yet challenging task. For instance, one might be interested in how a pension plan af…
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Lucas Kook, Chris Kolb, Philipp Schiele +8
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithm…
Algorithm-agnostic significance testing in supervised learning with multimodal data
Lucas Kook, Anton Rask Lundborg
Valid statistical inference is crucial for decision-making but difficult to obtain in supervised learning with multimodal data, e.g., combinations of clinical features, genomic dat…
Model-based causal feature selection for general response types
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg +2
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal featur…
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Lucas Kook, Philipp FM Baumann, Oliver Dürr +2
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classi…
Deep conditional transformation models for survival analysis
Gabriele Campanella, Lucas Kook, Ida Häggström +2
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of…
Distribution-Free Location-Scale Regression
Sandra Siegfried, Lucas Kook, Torsten Hothorn
We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modelling for arbitrary outcom…
Deep transformation models for functional outcome prediction after acute ischemic stroke
Lisa Herzog, Lucas Kook, Andrea Götschi +6
In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and ima…
Deep interpretable ensembles
Lucas Kook, Andrea Götschi, Philipp FM Baumann +2
Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually…
Pitfalls and potentials in simulation studies: Questionable research practices in comparative simulation studies allow for spurious claims of superiority of any method
Samuel Pawel, Lucas Kook, Kelly Reeve
Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of…
Distributional Anchor Regression
Lucas Kook, Beate Sick, Peter Bühlmann
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable b…
Deep and interpretable regression models for ordinal outcomes
Lucas Kook, Lisa Herzog, Torsten Hothorn +2
Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning…