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pro vyhledávání: '"Kook, Lucas"'
Autor:
Kook, Lucas, Pfister, Niklas
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 affects not only the average savings among all individuals but also how
Externí odkaz:
http://arxiv.org/abs/2406.19986
Autor:
Kook, Lucas, Kolb, Chris, Schiele, Philipp, Dold, Daniel, Arpogaus, Marcel, Fritz, Cornelius, Baumann, Philipp F., Kopper, Philipp, Pielok, Tobias, Dorigatti, Emilio, Rügamer, David
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models
Externí odkaz:
http://arxiv.org/abs/2405.05429
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 was, for instance, shown that neural networks can deduce racial infor
Externí odkaz:
http://arxiv.org/abs/2405.02475
Autor:
Kook, Lucas, Lundborg, Anton Rask
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 data, and medical images. Multimodal data often warrants the use of black
Externí odkaz:
http://arxiv.org/abs/2402.14416
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 feature selection which requires data from heterogeneous settings and exploi
Externí odkaz:
http://arxiv.org/abs/2309.12833
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load o
Externí odkaz:
http://arxiv.org/abs/2211.13665
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 electronic health records. Recently, several neural-network based solu
Externí odkaz:
http://arxiv.org/abs/2210.11366
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 outcomes. We replace the strict parametric distribution formulating such a m
Externí odkaz:
http://arxiv.org/abs/2208.05302
Autor:
Herzog, Lisa, Kook, Lucas, Götschi, Andrea, Petermann, Katrin, Hänsel, Martin, Hamann, Janne, Dürr, Oliver, Wegener, Susanne, Sick, Beate
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 image data. We show how to apply deep transformation models (DTMs) for di
Externí odkaz:
http://arxiv.org/abs/2206.13302
Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-
Externí odkaz:
http://arxiv.org/abs/2205.12729