Machine Learning for Sequential Data: Unraveling the Challenges Associated with Feature Encoding, Output Decoding, and Distribution Shifts / Matthew Caron ; Oliver Müller, Markus Weinmann. Paderborn, 2025
Inhalt
- List of Figures
- List of Tables
- Part A: Synopsis
- Introduction
- Research Background
- Research Contributions
- Overview
- Paper 1 – Hardening Soft Information
- Paper 2 – PIVOT: A Framework for Valuing Actions in Handball
- Paper 3 – To the Moon! Analyzing the Community of "Degenerates"
- Paper 4 – Towards Transparent Data-Driven Brand Valuation
- Paper 5 – Shortcut Learning in Financial Text Mining
- Paper 6 – Integrating Driver Behavior into Last-Mile Delivery Routing
- Paper 7 – TacticalGPT: LLMs for Tactical Decisions in Football
- Paper 8 – Detecting and Mitigating Shortcut Learning Bias in IS Research
- Discussion & Conclusion
- Part B: Research Papers
- Hardening Soft Information: A Transformer-Based Approach toForecasting Stock Return Volatility (Caron and Müller, 2020)
- PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data (Müller et al., 2021)
- To the Moon! Analyzing the Community of "Degenerates" Engagedin the Surge of the GME Stock (Caron et al., 2021)
- Towards a Reliable & Transparent Approach to Data-DrivenBrand Valuation (Caron et al., 2022)
- Shortcut Learning in Financial Text Mining: Exposing the OverlyOptimistic Performance Estimates of Text Classification Modelsunder Distribution Shift (Caron, 2022)
- Integrating Driver Behavior into Last-Mile Delivery Routing:Combining Machine Learning and Optimization in a HybridDecision Support Framework (Dieter et al., 2023)
- TacticalGPT: Uncovering the Potential of LLMs for PredictingTactical Decisions in Professional Football (Caron and Müller, 2023)
- Detecting and Mitigating Shortcut Learning Bias in Machine Learning: A Pathway to More Generalizable ML-based (IS) Research(Caron et al., 2025)
- Bibliography