[{"file":[{"file_name":"Driver_Behavior_in_last_mile_delivery_EJOR_Final.pdf","date_created":"2023-05-03T08:48:57Z","access_level":"open_access","file_size":1162912,"creator":"mateskam","file_id":"44388","content_type":"application/pdf","date_updated":"2023-05-03T22:01:15Z","relation":"main_file"}],"author":[{"first_name":"Peter","full_name":"Dieter, Peter","last_name":"Dieter","id":"88592"},{"full_name":"Caron, Matthew","first_name":"Matthew","id":"60721","last_name":"Caron"},{"first_name":"Guido","full_name":"Schryen, Guido","last_name":"Schryen","id":"72850"}],"file_date_updated":"2023-05-03T22:01:15Z","publication":"European Journal of Operational Research (EJOR)","status":"public","has_accepted_license":"1","date_created":"2023-05-03T08:55:42Z","volume":311,"user_id":"72850","ddc":["000"],"type":"journal_article","citation":{"chicago":"Dieter, Peter, Matthew Caron, and Guido Schryen. “Integrating Driver Behavior into Last-Mile Delivery Routing: Combining Machine Learning and Optimization in a Hybrid Decision Support Framework.” European Journal of Operational Research (EJOR) 311, no. 1 (2023): 283–300. https://doi.org/10.1016/j.ejor.2023.04.043.","ama":"Dieter P, Caron M, Schryen G. Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework. European Journal of Operational Research (EJOR). 2023;311(1):283-300. doi:https://doi.org/10.1016/j.ejor.2023.04.043","short":"P. Dieter, M. Caron, G. Schryen, European Journal of Operational Research (EJOR) 311 (2023) 283–300.","apa":"Dieter, P., Caron, M., & Schryen, G. (2023). Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework. European Journal of Operational Research (EJOR), 311(1), 283–300. https://doi.org/10.1016/j.ejor.2023.04.043","mla":"Dieter, Peter, et al. “Integrating Driver Behavior into Last-Mile Delivery Routing: Combining Machine Learning and Optimization in a Hybrid Decision Support Framework.” European Journal of Operational Research (EJOR), vol. 311, no. 1, 2023, pp. 283–300, doi:https://doi.org/10.1016/j.ejor.2023.04.043.","bibtex":"@article{Dieter_Caron_Schryen_2023, title={Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework}, volume={311}, DOI={https://doi.org/10.1016/j.ejor.2023.04.043}, number={1}, journal={European Journal of Operational Research (EJOR)}, author={Dieter, Peter and Caron, Matthew and Schryen, Guido}, year={2023}, pages={283–300} }","ieee":"P. Dieter, M. Caron, and G. Schryen, “Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework,” European Journal of Operational Research (EJOR), vol. 311, no. 1, pp. 283–300, 2023, doi: https://doi.org/10.1016/j.ejor.2023.04.043."},"year":"2023","page":"283-300","_id":"44383","intvolume":" 311","issue":"1","department":[{"_id":"277"}],"title":"Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework","language":[{"iso":"eng"}],"date_updated":"2023-06-20T07:00:58Z","oa":"1","doi":"https://doi.org/10.1016/j.ejor.2023.04.043"},{"main_file_link":[{"url":"https://scholarspace.manoa.hawaii.edu/items/2ddab486-5d2f-4302-8de3-a8b24017da3d","open_access":"1"}],"language":[{"iso":"eng"}],"citation":{"ieee":"H. Halimeh, M. Caron, and O. Müller, “Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features,” presented at the Hawaii International Conference on System Sciences, 2023.","short":"H. Halimeh, M. Caron, O. Müller, in: Hawaii International Conference on System Sciences, 2023.","mla":"Halimeh, Haya, et al. “Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features.” Hawaii International Conference on System Sciences, 2023.","bibtex":"@inproceedings{Halimeh_Caron_Müller_2023, title={Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}, booktitle={Hawaii International Conference on System Sciences}, author={Halimeh, Haya and Caron, Matthew and Müller, Oliver}, year={2023} }","chicago":"Halimeh, Haya, Matthew Caron, and Oliver Müller. “Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features.” In Hawaii International Conference on System Sciences, 2023.","apa":"Halimeh, H., Caron, M., & Müller, O. (2023). Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features. Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences.","ama":"Halimeh H, Caron M, Müller O. Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features. In: Hawaii International Conference on System Sciences. ; 2023."},"year":"2023","type":"conference","_id":"45270","date_updated":"2024-01-10T15:16:37Z","conference":{"name":"Hawaii International Conference on System Sciences","start_date":"2023-01-03","end_date":"2023-01-06"},"oa":"1","author":[{"last_name":"Halimeh","id":"87673","first_name":"Haya","full_name":"Halimeh, Haya"},{"id":"60721","last_name":"Caron","full_name":"Caron, Matthew","first_name":"Matthew"},{"first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller","id":"72849"}],"department":[{"_id":"195"},{"_id":"196"}],"publication":"Hawaii International Conference on System Sciences","keyword":["Social Media and Healthcare Technology","early depression detection","liwc","mental health","transfer learning","transformer architectures"],"status":"public","date_created":"2023-05-25T10:25:21Z","publication_status":"published","abstract":[{"lang":"eng","text":"Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies."}],"user_id":"60721","related_material":{"link":[{"relation":"confirmation","url":"https://hdl.handle.net/10125/103046"}]},"title":"Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features"},{"conference":{"end_date":"2023-09-21","start_date":"2023-09-18","name":"Wirtschaftsinformatik","location":"Paderborn, Germany"},"_id":"50437","date_updated":"2024-01-10T15:20:13Z","main_file_link":[{"url":"https://aisel.aisnet.org/wi2023/100/"}],"type":"conference","year":"2023","citation":{"ieee":"R. Reimann and M. Caron, “Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach,” presented at the Wirtschaftsinformatik, Paderborn, Germany, 2023.","short":"R. Reimann, M. Caron, in: Wirtschaftsinformatik, 2023.","mla":"Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach.” Wirtschaftsinformatik, 2023.","bibtex":"@inproceedings{Reimann_Caron_2023, title={Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach}, booktitle={Wirtschaftsinformatik}, author={Reimann, Raphael and Caron, Matthew}, year={2023} }","chicago":"Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach.” In Wirtschaftsinformatik, 2023.","ama":"Reimann R, Caron M. Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach. In: Wirtschaftsinformatik. ; 2023.","apa":"Reimann, R., & Caron, M. (2023). Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach. Wirtschaftsinformatik. Wirtschaftsinformatik, Paderborn, Germany."},"language":[{"iso":"eng"}],"abstract":[{"text":"The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs.","lang":"eng"}],"title":"Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach","user_id":"60721","publication":"Wirtschaftsinformatik","department":[{"_id":"196"}],"author":[{"first_name":"Raphael","full_name":"Reimann, Raphael","last_name":"Reimann"},{"last_name":"Caron","id":"60721","first_name":"Matthew","full_name":"Caron, Matthew"}],"publication_status":"published","date_created":"2024-01-10T15:15:19Z","status":"public"},{"user_id":"60721","title":"Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation","place":"Minneapolis, USA","abstract":[{"text":"Now accounting for more than 80% of a firm's worth, brands have become essential assets for modern organizations. However, methods and techniques for the monetary valuation of brands are still under-researched. Hence, the objective of this study is to evaluate the utility of explanatory statistical models and machine learning approaches for explaining and predicting brand value. Drawing upon the case of the most valuable English football brands during the 2016/17 to 2020/21 seasons, we demonstrate how to operationalize Aaker's (1991) theoretical brand equity framework to collect meaningful qualitative and quantitative feature sets. Our explanatory models can explain up to 77% of the variation in brand valuations across all clubs and seasons, while our predictive approach can predict out-of-sample observations with a mean absolute percentage error (MAPE) of 14%. Future research can build upon our results to develop domain-specific brand valuation methods while enabling managers to make better-informed investment decisions.","lang":"eng"}],"status":"public","date_created":"2023-02-02T13:34:49Z","publication_status":"published","author":[{"full_name":"Caron, Matthew","first_name":"Matthew","id":"60721","last_name":"Caron"},{"id":"49160","last_name":"Bartelheimer","full_name":"Bartelheimer, Christian","first_name":"Christian"},{"first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller","id":"72849"}],"department":[{"_id":"195"}],"publication":"Proceeding of the 28th Americas Conference on Information Systems (AMCIS)","_id":"41486","date_updated":"2023-02-28T08:59:38Z","conference":{"end_date":"2022-08-14","start_date":"2022-08-10","name":"28th Americas Conference on Information Systems (AMCIS)","location":"Minneapolis, USA"},"language":[{"iso":"eng"}],"year":"2022","type":"conference","citation":{"ieee":"M. Caron, C. Bartelheimer, and O. Müller, “Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation,” presented at the 28th Americas Conference on Information Systems (AMCIS), Minneapolis, USA, 2022.","short":"M. Caron, C. Bartelheimer, O. Müller, in: Proceeding of the 28th Americas Conference on Information Systems (AMCIS), Minneapolis, USA, 2022.","bibtex":"@inproceedings{Caron_Bartelheimer_Müller_2022, place={Minneapolis, USA}, title={Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation}, booktitle={Proceeding of the 28th Americas Conference on Information Systems (AMCIS)}, author={Caron, Matthew and Bartelheimer, Christian and Müller, Oliver}, year={2022} }","mla":"Caron, Matthew, et al. “Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation.” Proceeding of the 28th Americas Conference on Information Systems (AMCIS), 2022.","chicago":"Caron, Matthew, Christian Bartelheimer, and Oliver Müller. “Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation.” In Proceeding of the 28th Americas Conference on Information Systems (AMCIS). Minneapolis, USA, 2022.","apa":"Caron, M., Bartelheimer, C., & Müller, O. (2022). Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation. Proceeding of the 28th Americas Conference on Information Systems (AMCIS). 28th Americas Conference on Information Systems (AMCIS), Minneapolis, USA.","ama":"Caron M, Bartelheimer C, Müller O. Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation. In: Proceeding of the 28th Americas Conference on Information Systems (AMCIS). ; 2022."},"main_file_link":[{"url":"https://aisel.aisnet.org/amcis2022/conf_theme/conf_theme/10/"}]},{"citation":{"bibtex":"@inproceedings{Caron_2022, title={Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift}, DOI={10.1109/bigdata55660.2022.10020933}, booktitle={2022 IEEE International Conference on Big Data (Big Data)}, publisher={IEEE}, author={Caron, Matthew}, year={2022} }","mla":"Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift.” 2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022, doi:10.1109/bigdata55660.2022.10020933.","chicago":"Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift.” In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. https://doi.org/10.1109/bigdata55660.2022.10020933.","ama":"Caron M. Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift. In: 2022 IEEE International Conference on Big Data (Big Data). IEEE; 2022. doi:10.1109/bigdata55660.2022.10020933","apa":"Caron, M. (2022). Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift. 2022 IEEE International Conference on Big Data (Big Data). 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan. https://doi.org/10.1109/bigdata55660.2022.10020933","ieee":"M. Caron, “Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift,” presented at the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, doi: 10.1109/bigdata55660.2022.10020933.","short":"M. Caron, in: 2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022."},"type":"conference","year":"2022","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/10020933"}],"doi":"10.1109/bigdata55660.2022.10020933","date_updated":"2024-01-15T12:32:06Z","_id":"42631","conference":{"location":"Osaka, Japan","name":"2022 IEEE International Conference on Big Data (Big Data)","start_date":"2022-12-17","end_date":"2022-12-20"},"publication_status":"published","publication_identifier":{"eisbn":["978-1-6654-8045-1"]},"status":"public","date_created":"2023-02-28T08:29:30Z","publisher":"IEEE","author":[{"last_name":"Caron","id":"60721","first_name":"Matthew","full_name":"Caron, Matthew"}],"department":[{"_id":"196"}],"publication":"2022 IEEE International Conference on Big Data (Big Data)","title":"Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift","user_id":"60721","abstract":[{"lang":"eng","text":"In recent years, many cases of deep neural networks failing dramatically when faced with adversarial or real-world examples have been reported. Such failures, which are quite hard to detect, are often related to a generalization problem known as shortcut learning. Yet, with state-of-the-art transformer models now being ubiquitous in financial text mining, one cannot help but wonder how reliable the results conveyed in the ever-growing literature genuinely are. Against this background, we expose, in this work, how vulnerable contemporary financial text mining approaches are to shortcut learning. Focussing on the common learning task of financial sentiment classification, we assess, using two entity-based sampling strategies and our publicly-available dataset, the discrepancies between i.i.d. and o.o.d. performance estimates of four transformer models. Our results reveal that o.o.d. performance estimates are consistently weaker than those of their i.i.d. counterparts, with the error rate increasing by as much as 29.7%, thus, demonstrating how this issue can, when overlooked, lead to misleading evaluations. Moreover, we show how additional preprocessing steps, such as entity removal and vocabulary filtering, can help reduce the effects of shortcut learning by filtering out entity-related linguistic cues."}]},{"status":"public","date_created":"2021-09-29T10:06:24Z","publication_status":"published","author":[{"full_name":"Caron, Matthew","first_name":"Matthew","id":"60721","last_name":"Caron"},{"first_name":"Frederik S.","full_name":"Bäumer, Frederik S.","last_name":"Bäumer"},{"id":"72849","last_name":"Müller","full_name":"Müller, Oliver","first_name":"Oliver"}],"department":[{"_id":"196"}],"publication":"55th Hawaii International Conference on System Sciences (HICSS)","user_id":"60721","title":"Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models","abstract":[{"text":"Our world is more connected than ever before. Sadly, however, this highly connected world has made it easier to bully, insult, and propagate hate speech on the cyberspace. Even though researchers and companies alike have started investigating this real-world problem, the question remains as to why users are increasingly being exposed to hate and discrimination online. In fact, the noticeable and persistent increase in harmful language on social media platforms indicates that the situation is, actually, only getting worse. Hence, in this work, we show that contemporary ML methods can help tackle this challenge in an accurate and cost-effective manner. Our experiments demonstrate that a universal approach combining transfer learning methods and state-of-the-art Transformer architectures can trigger the efficient development of toxic language detection models. Consequently, with this universal approach, we provide platform providers with a simplistic approach capable of enabling the automated moderation of user-generated content, and as a result, hope to contribute to making the web a safer place.","lang":"eng"}],"language":[{"iso":"eng"}],"type":"conference","year":"2022","citation":{"bibtex":"@inproceedings{Caron_Bäumer_Müller_2022, title={Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models}, booktitle={55th Hawaii International Conference on System Sciences (HICSS)}, author={Caron, Matthew and Bäumer, Frederik S. and Müller, Oliver}, year={2022} }","mla":"Caron, Matthew, et al. “Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models.” 55th Hawaii International Conference on System Sciences (HICSS), 2022.","chicago":"Caron, Matthew, Frederik S. Bäumer, and Oliver Müller. “Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models.” In 55th Hawaii International Conference on System Sciences (HICSS), 2022.","ama":"Caron M, Bäumer FS, Müller O. Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models. In: 55th Hawaii International Conference on System Sciences (HICSS). ; 2022.","apa":"Caron, M., Bäumer, F. S., & Müller, O. (2022). Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models. 55th Hawaii International Conference on System Sciences (HICSS). 55th Hawaii International Conference on System Sciences (HICSS), Online.","ieee":"M. Caron, F. S. Bäumer, and O. Müller, “Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models,” presented at the 55th Hawaii International Conference on System Sciences (HICSS), Online, 2022.","short":"M. Caron, F.S. Bäumer, O. Müller, in: 55th Hawaii International Conference on System Sciences (HICSS), 2022."},"main_file_link":[{"url":"http://hdl.handle.net/10125/79428"}],"date_updated":"2024-01-15T12:37:10Z","_id":"25113","conference":{"location":"Online","start_date":"2022-01-03","name":"55th Hawaii International Conference on System Sciences (HICSS)","end_date":"2022-01-07"}},{"date_updated":"2023-02-28T08:58:24Z","_id":"24547","conference":{"location":"Online","start_date":"2021-09-13","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021)","end_date":"2021-09-17"},"main_file_link":[{"url":"https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf"}],"type":"conference","citation":{"mla":"Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).","bibtex":"@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }","apa":"Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","ama":"Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).","chicago":"Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.","ieee":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","short":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d."},"year":"2021","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time."}],"title":"PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data","user_id":"60721","author":[{"id":"72849","last_name":"Müller","full_name":"Müller, Oliver","first_name":"Oliver"},{"last_name":"Caron","id":"60721","first_name":"Matthew","full_name":"Caron, Matthew"},{"last_name":"Döring","full_name":"Döring, Michael","first_name":"Michael"},{"last_name":"Heuwinkel","first_name":"Tim","full_name":"Heuwinkel, Tim"},{"id":"46","last_name":"Baumeister","orcid":"0000-0003-2683-5826","full_name":"Baumeister, Jochen","first_name":"Jochen"}],"publication":"8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)","keyword":["expected possession value","handball","tracking data","time series classification","deep learning"],"department":[{"_id":"196"},{"_id":"172"}],"publication_status":"inpress","status":"public","date_created":"2021-09-16T08:33:04Z"},{"language":[{"iso":"eng"}],"year":"2021","citation":{"mla":"Caron, Matthew, et al. “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” 42nd International Conference on Information Systems (ICIS 2021), 2021.","bibtex":"@inproceedings{Caron_Gulenko_Müller_2021, title={To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock}, booktitle={42nd International Conference on Information Systems (ICIS 2021)}, author={Caron, Matthew and Gulenko, Maryna and Müller, Oliver}, year={2021} }","chicago":"Caron, Matthew, Maryna Gulenko, and Oliver Müller. “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” In 42nd International Conference on Information Systems (ICIS 2021), 2021.","ama":"Caron M, Gulenko M, Müller O. To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock. In: 42nd International Conference on Information Systems (ICIS 2021). ; 2021.","apa":"Caron, M., Gulenko, M., & Müller, O. (2021). To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock. 42nd International Conference on Information Systems (ICIS 2021). 42nd International Conference on Information Systems (ICIS 2021), Austin, Texas.","ieee":"M. Caron, M. Gulenko, and O. Müller, “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock,” presented at the 42nd International Conference on Information Systems (ICIS 2021), Austin, Texas, 2021.","short":"M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information Systems (ICIS 2021), 2021."},"type":"conference","main_file_link":[{"url":"https://aisel.aisnet.org/icis2021/social_media/social_media/13/"}],"conference":{"end_date":"2021-12-15","name":"42nd International Conference on Information Systems (ICIS 2021)","start_date":"2021-12-12","location":"Austin, Texas"},"_id":"25029","date_updated":"2023-02-28T08:58:16Z","date_created":"2021-09-24T09:51:35Z","status":"public","publication_status":"published","publication":"42nd International Conference on Information Systems (ICIS 2021)","keyword":["Retail investors","GameStop","Social Networks","Reddit","WallStreetBets"],"department":[{"_id":"196"}],"author":[{"full_name":"Caron, Matthew","first_name":"Matthew","id":"60721","last_name":"Caron"},{"first_name":"Maryna","full_name":"Gulenko, Maryna","last_name":"Gulenko","id":"64226"},{"first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller","id":"72849"}],"user_id":"60721","title":"To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock","abstract":[{"text":"In early 2021, the finance world was taken by storm by the dramatic price surge of the GameStop Corp. stock. This rise is being, at least in part, attributed to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit group. In this work, we set out to address if user activity on the WSB subreddit is associated with the trading volume of the GME stock. Leveraging a unique dataset containing more than 4.9 million WSB posts and comments, we assert that user activity is associated with the trading volume of the GameStop stock. We further show that posts have a significantly higher predictive power than comments and are especially helpful for predicting unusually high trading volume. Lastly, as recent events have shown, we believe that these findings have implications for retail and institutional investors, trading platforms, and policymakers, as these can have disruptive potential.","lang":"eng"}]},{"status":"public","date_created":"2021-03-24T13:09:55Z","publication_status":"published","publication_identifier":{"eisbn":["978-1-7281-6251-5"]},"author":[{"full_name":"Caron, Matthew","first_name":"Matthew","id":"60721","last_name":"Caron"},{"last_name":"Müller","id":"72849","first_name":"Oliver","full_name":"Müller, Oliver"}],"publication":"2020 IEEE International Conference on Big Data (Big Data)","department":[{"_id":"196"}],"user_id":"60721","title":"Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility","abstract":[{"lang":"eng","text":"Historically, the field of financial forecasting almost exclusively relied on so-called hard information – i.e., numerical data with well-defined and unambiguous meaning. Over the last few decades, however, researchers and practitioners alike have, following the advances in natural language understanding, started recognizing the benefits of integrating soft information into financial modelling. In line with the above, this paper examines whether contemporary attention-based sequence-to-sequence models, known as Transformers, can help improve stock return volatility prediction when applied to corporate annual reports. Using a publicly available benchmark dataset, we show, in an empirical analysis, that out-of-the-box Transformer models have the ability to outmatch current state-of-the-art results and, more importantly, that our proposed feature-based Transformer approach can outperform a robust numerical baseline. To the best of our knowledge, this is the first empirical study focusing on stock return volatility prediction (1) to ever experiment with state-of-the-art Transformer architectures and (2) to demonstrate that a model based solely on soft information can surpass its numerical counterpart. Furthermore, we show that by including an additional numerical feature into our best text-only model, we can push the performance of our model even further, suggesting that soft and hard information contain different predictive signals."}],"language":[{"iso":"eng"}],"citation":{"short":"M. Caron, O. Müller, in: 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4383–4391.","ieee":"M. Caron and O. Müller, “Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility,” in 2020 IEEE International Conference on Big Data (Big Data), Online, 2020, pp. 4383–4391, doi: 10.1109/BigData50022.2020.9378134.","ama":"Caron M, Müller O. Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility. In: 2020 IEEE International Conference on Big Data (Big Data). ; 2020:4383-4391. doi:10.1109/BigData50022.2020.9378134","apa":"Caron, M., & Müller, O. (2020). Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility. 2020 IEEE International Conference on Big Data (Big Data), 4383–4391. https://doi.org/10.1109/BigData50022.2020.9378134","chicago":"Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility.” In 2020 IEEE International Conference on Big Data (Big Data), 4383–91, 2020. https://doi.org/10.1109/BigData50022.2020.9378134.","bibtex":"@inproceedings{Caron_Müller_2020, title={Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility}, DOI={10.1109/BigData50022.2020.9378134}, booktitle={2020 IEEE International Conference on Big Data (Big Data)}, author={Caron, Matthew and Müller, Oliver}, year={2020}, pages={4383–4391} }","mla":"Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility.” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4383–91, doi:10.1109/BigData50022.2020.9378134."},"year":"2020","type":"conference","page":"4383-4391","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9378134"}],"doi":"10.1109/BigData50022.2020.9378134","date_updated":"2024-01-15T12:32:37Z","_id":"21563","conference":{"end_date":"2020-12-13","location":"Online","start_date":"2020-12-10","name":"2020 IEEE International Conference on Big Data (Big Data)"}}]