1 citations · 3 across the 15 of their papers we have counts for
6 papers · 1 filter
On the Potential of Multi-Task Learning in Predictive Process Monitoring
Lukas Kirchdorfer, Keyvan Amiri Elyasi, Heiner Stuckenschmidt
Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support toward proactive analysis a…
Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
Andrej Tschalzev, Stefan Lüdtke, Heiner Stuckenschmidt +1
Tabular machine learning benchmarks typically summarize performance by averaging scores, ranks, or pairwise wins across datasets. Such aggregates are useful for selecting robust de…
Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes
Keyvan Amiri Elyasi, Lukas Kirchdorfer, Heiner Stuckenschmidt
The early detection of delayed cases in business processes is a critical capability for organizations. Predictive process monitoring (PPM) supports this task by using historical ev…
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 Özdemir, 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…