Hyperparameter Selection in Continual Learning
Abstract: In continual learning (CL)---where a learner trains on a stream of data---standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the whole data stream with different hyperparameter settings. However, this end-of-training HPO is unrealistic as in practice a learner can only see the stream once. Hence, there is an open question: what HPO framework should a practitioner use for a CL problem in reality? This paper answers this question by evaluating several realistic HPO frameworks. We find that all the HPO frameworks considered, including end-of-training HPO, perform similarly on common CL benchmarks. We therefore advocate using the realistic and most computationally efficient method: fitting the hyperparameters on the first task and then fixing them throughout training.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Andreas_Kirsch1
Submission Number: 2499
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