Limits of Transfer Learning
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer learning remains less well-developed. To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems. Furthermore, we prove how the degree of probabilistic change in an algorithm using transfer learning places an upper bound on the amount of improvement possible. These results build on the algorithmic search framework for machine learning, allowing the results to apply to a wide range of learning problems using transfer.
Code (0)
등록된 구현이 없습니다.
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
Evaluating Load Models and Their Impacts on Power Transfer Limits
Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line…
Q-LearningA general method for estimating zonal transmission interface limits from nodal network data
Capacity expansion models for the electric power system often employ zonal (rather than nodal) resolution, necessitating estimates of aggregate power transfer limits across the interfaces between model zones. Interface l…
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
Can graph neural networks generalize to graphs that are different from the graphs they were trained on, e.g., in size? In this work, we study this question from a theoretical perspective. While recent work established su…
Transfer Learning for Linear Discriminant Analysis with a Shared Classification Signal
This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source domains, where the mean difference in each domain is de…
Transfer LearningMinimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Transfer learning has emerged as a powerful technique for improving the performance of machine learning models on new domains where labeled training data may be scarce. In this approach a model trained for a source task,…
Transfer Learning