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cs.LG2026
Continual Learning of Domain-Invariant Representations
Pascal Janetzky, Tobias Schlagenhauf, Stefan Feuerriegel
Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL methods optimize for in-domain…
cs.LG2026
ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
Maresa Schröder, Pascal Janetzky, Michael Klar +1
Bayesian optimization is widely used for hyperparameter optimization when model evaluations are expensive; however, noisy acquisition estimates can lead to unstable decisions. We i…
cs.LG2025
Slowing Down Forgetting in Continual Learning
Pascal Janetzky, Tobias Schlagenhauf, Stefan Feuerriegel
A common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned. In this paper, we propos…