no code implementations • 23 Mar 2024 • Lukas Vöge, Vincent Gurgul, Stefan Lessmann
This paper introduces a novel approach for efficiently distilling LLMs into smaller, application-specific models, significantly reducing operational costs and manual labor.
no code implementations • 8 Dec 2023 • Björn Bokelmann, Stefan Lessmann
In our research, we show that heteroskedastictity in the training data can cause a bias of the uplift model ranking: individuals with the highest treatment effects can get accumulated in large numbers at the bottom of the ranking.
no code implementations • 23 Nov 2023 • Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle
Our findings reveal that incorporating NLP data significantly enhances the forecasting performance of our models.
no code implementations • 1 Aug 2023 • Savina Kim, Stefan Lessmann, Galina Andreeva, Michael Rovatsos
Drawing from the intersectionality paradigm, the study examines intersectional horizontal inequities in credit access by gender, age, marital status, single parent status and number of children.
no code implementations • 21 Jul 2023 • Christopher Gerling, Stefan Lessmann
In sum, the paper contributes original empirical evidence on the effectiveness and efficiency of multi-model models for document processing in the banking business and offers practical guidance on how to unlock this potential in day-to-day operations.
1 code implementation • 19 May 2023 • Victor Medina-Olivares, Stefan Lessmann, Nadja Klein
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.
no code implementations • 2 Nov 2022 • Wei Li, Wolfgang Karl Härdle, Stefan Lessmann
In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction.
1 code implementation • 5 Oct 2022 • Björn Bokelmann, Stefan Lessmann
We theoretically analyze the variance of uplift evaluation metrics and derive possible methods of variance reduction, which are based on statistical adjustment of the outcome.
no code implementations • 6 Apr 2022 • Duygu Ider, Stefan Lessmann
Anticipating price developments in financial markets is a topic of continued interest in forecasting.
no code implementations • 15 Dec 2021 • Justin Hellermann, Stefan Lessmann
To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP).
1 code implementation • 22 Nov 2021 • Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph
Recurrent neural networks (RNNs) are a popular choice for modeling sequential data.
no code implementations • 30 May 2021 • Franziska Scherpinski, Stefan Lessmann
To fill this gap and raise business performance, this paper introduces an RS with a personalized long ranking (top-N).
1 code implementation • 2 Mar 2021 • Nikita Kozodoi, Johannes Jacob, Stefan Lessmann
Yet, the literature on fair ML in credit scoring is scarce.
no code implementations • 9 Jan 2021 • Robin M. Gubela, Stefan Lessmann
Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment.
no code implementations • 15 Sep 2020 • Marius Lux, Wolfgang Karl Härdle, Stefan Lessmann
The SVR-GARCH-KDE hybrid is compared to standard, exponential and threshold GARCH models coupled with different error distributions.
1 code implementation • 20 Aug 2020 • Justin Engelmann, Stefan Lessmann
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models.
2 code implementations • 13 Mar 2020 • Johannes Haupt, Stefan Lessmann
This study provides a formal analysis of the customer targeting problem when the cost for a marketing action depends on the customer response and proposes a framework to estimate the decision variables for campaign profit optimization.
no code implementations • 20 Nov 2019 • Robin M. Gubela, Stefan Lessmann, Szymon Jaroszewicz
The proposed methodology entails a transformation of the prediction target, customer-level revenues, that facilitates implementing a causal uplift model using standard machine learning algorithms.
1 code implementation • 1 Oct 2019 • Johannes Haupt, Daniel Jacob, Robin M. Gubela, Stefan Lessmann
To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization.
1 code implementation • 24 Sep 2019 • C. Gary Mena, Arno De Caigny, Kristof Coussement, Koen W. De Bock, Stefan Lessmann
Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an aggregation procedure of such sequential data.
no code implementations • 13 Sep 2019 • Nikita Kozodoi, Panagiotis Katsas, Stefan Lessmann, Luis Moreira-Matias, Konstantinos Papakonstantinou
First, we propose a self-learning framework for reject inference.
no code implementations • 14 Dec 2018 • Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann, Tiejun Ma, Ming-Chien Sung, Johnnie E. V. Johnson
The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.
no code implementations • 24 Jul 2017 • Norman Hiob, Stefan Lessmann
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records.
no code implementations • 10 Jul 2017 • Korbinian Dress, Stefan Lessmann, Hans-Jörg von Mettenheim
Leasing is a popular channel to market new cars.