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Found 5 papers, 3 papers with code
Communication-Efficient Agnostic Federated Averaging
We propose a communication-efficient distributed algorithm called Agnostic Federated Averaging (or AgnosticFedAvg) to minimize the domain-agnostic objective proposed in Mohri et al. (2019), which is amenable to other private mechanisms such as secure aggregation.
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search.
Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph Entities
Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search.
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains.
Three Approaches for Personalization with Applications to Federated Learning
The standard objective in machine learning is to train a single model for all users.
