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Found 5 papers, 2 papers with code
Margin-aware Adversarial Domain Adaptation with Optimal Transport
In this paper, we propose a new theoretical analysis of unsupervised domain adaptation that relates notions of large margin separation, adversarial learning and optimal transport.
A Swiss Army Knife for Minimax Optimal Transport
The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community.
Meta Learning in Bandits within Shared Affine Subspaces
We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits.
Hypothesis Transfer in Bandits by Weighted Models
We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning.
Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization
Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance.
