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Tobias Schlagenhauf

2 accepted papers

2026

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

ICLR 2026poster

Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictably across datasets. Recent studies highlight the surprising competitiveness of simple linear models, suggesting that their robustness and interpretability wa…

Cited by 0SourcecodeScholar
2025

Federated Learning with Heterogeneous Feature Adaptation for Human Activity Recognition

ICASSP 2025accepted

Federated learning promotes knowledge sharing in data-sensitive domains, such as Human Activity Recognition (HAR). However, data heterogeneity, namely, non-iid feature, can degrade the performance by causing client drift. We propose an effective knowledge distillation method incorporating a novel ba…

Cited by 0SourceScholar