Zobrazeno 1 - 10
of 268
pro vyhledávání: '"Lessmann, Stefan"'
Autor:
Velev, Georg, Lessmann, Stefan
Nonlinear causal discovery from observational data imposes strict identifiability assumptions on the formulation of structural equations utilized in the data generating process. The evaluation of structure learning methods under assumption violations
Externí odkaz:
http://arxiv.org/abs/2409.19377
Autor:
Kozodoi, Nikita, Lessmann, Stefan, Alamgir, Morteza, Moreira-Matias, Luis, Papakonstantinou, Konstantinos
Scoring models support decision-making in financial institutions. Their estimation and evaluation are based on the data of previously accepted applicants with known repayment behavior. This creates sampling bias: the available labeled data offers a p
Externí odkaz:
http://arxiv.org/abs/2407.13009
This paper introduces a novel approach for efficiently distilling LLMs into smaller, application-specific models, significantly reducing operational costs and manual labor. Addressing the challenge of deploying computationally intensive LLMs in speci
Externí odkaz:
http://arxiv.org/abs/2403.15886
Autor:
Bokelmann, Björn, Lessmann, Stefan
In many business applications, including online marketing and customer churn prevention, randomized controlled trials (RCT's) are conducted to investigate on the effect of specific treatment (coupon offers, advertisement mailings,...). Such RCT's all
Externí odkaz:
http://arxiv.org/abs/2401.14294
Autor:
Bokelmann, Björn, Lessmann, Stefan
There are various applications, where companies need to decide to which individuals they should best allocate treatment. To support such decisions, uplift models are applied to predict treatment effects on an individual level. Based on the predicted
Externí odkaz:
http://arxiv.org/abs/2312.05234
The increasing usage of new data sources and machine learning (ML) technology in credit modeling raises concerns with regards to potentially unfair decision-making that rely on protected characteristics (e.g., race, sex, age) or other socio-economic
Externí odkaz:
http://arxiv.org/abs/2308.02680
Autor:
Gerling, Christopher, Lessmann, Stefan
In response to growing FinTech competition and the need for improved operational efficiency, this research focuses on understanding the potential of advanced document analytics, particularly using multimodal models, in banking processes. We perform a
Externí odkaz:
http://arxiv.org/abs/2307.11845
We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our approach allows for non-linear relationships and high-dimensional interac
Externí odkaz:
http://arxiv.org/abs/2305.11575
Autor:
Bokelmann, Björn, Lessmann, Stefan
Many forecasting applications have a limited distributed target variable, which is zero for most observations and positive for the remaining observations. In the econometrics literature, there is much research about statistical model building for lim
Externí odkaz:
http://arxiv.org/abs/2303.14288
There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practical implementation. This study proposes a data-driven explainable case-
Externí odkaz:
http://arxiv.org/abs/2211.00921