1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CE2026
Predicting Heterogeneous Treatment Effects Of Building Energy Saving Retrofits Using Causal Machine Learning
Kevin Zalipski, David Zapata Gonzalez, Oliver Müller
Information Systems research increasingly relies on machine learning (ML) to predict outcomes in complex sociotechnical systems, yet predictive models are not designed to identify…
cs.LG2026★ 1 cited
Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges
Marcel Meyer, Sascha Kaltenpoth, Kevin Zalipski +1
Time Series Foundation Models (TSFMs) represent a new paradigm for time-series forecasting, promising zero-shot predictions without the need for task-specific training or fine-tuni…