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Transfer learning has been proven effective when within-target labeled data is scarce. A lot of works have developed successful algorithms and empirically observed positive transfer effect that improves target generalization error using source knowledge.
Oct 14, 2018
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We provide new statistical guarantees for transfer learning via representation learning–when transfer is achieved by learning a feature representation shared.
Nov 24, 2018 · In this report, we do a survey of theoretical works in transfer learning and summarize key theo- retical guarantees that prove the effectiveness ...
On Theoretical Foundations for Transfer Learning. We aim to provide statistical guarantees for transfer learning in sequential settings under dynamic feedback.
Our learning bound improves over previous bounds for learning-to-learn [27] and by being fully data dependent, it can be used to evaluate the transfer.
Nov 22, 2023 · Assessing the effectiveness of transfer learning relies on understanding the similarity between the ground truth of the source and target tasks.
May 8, 2023 · In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms.
We provide new statistical guarantees for transfer learning via representation learning--when transfer is achieved by learning a feature representation shared ...
Abstract. We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from ...
Our theoretical analysis is based on algorithmic stability arguments allowing one to derive generalization guarantees when a learning algorithm does not suffer ...