8 papers
Mood Matters: How Syntactic Sensitivity Undermines Safety Alignment
Alina Klerings, Jannik Brinkmann, Heiner Stuckenschmidt +1
Large language models typically undergo post-training to align them with safety policies but there exist many sophisticated jailbreaks that sidestep established safeguards. For ins…
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
Konrad Ãzdemir, Julia Gastinger, Lukas Kirchdorfer +1
Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time. Yet, TKG forecasting models are commonly…
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
Lukas Kirchdorfer, Konrad Ãzdemir, Stjepan Kusenic +2
Business Process Simulation (BPS) is a critical tool for analyzing and improving organizational processes by estimating the impact of process changes. A key component of BPS is the…
Rethinking BPS: A Utility-Based Evaluation Framework
Konrad Ãzdemir, Lukas Kirchdorfer, Keyvan Amiri Elyasi +2
Business process simulation (BPS) is a key tool for analyzing and optimizing organizational workflows, supporting decision-making by estimating the impact of process changes. The r…
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke +3
Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approac…