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Found 6 papers, 1 papers with code
Statistically and Computationally Efficient Linear Meta-representation Learning
To cope with such data scarcity, meta-representation learning methods train across many related tasks to find a shared (lower-dimensional) representation of the data where all tasks can be solved accurately.
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning
We propose FedChain, an algorithmic framework that combines the strengths of local methods and global methods to achieve fast convergence in terms of R while leveraging the similarity between clients.
Efficient Algorithms for Federated Saddle Point Optimization
Our goal is to design an algorithm that can harness the benefit of similarity in the clients while recovering the Minibatch Mirror-prox performance under arbitrary heterogeneity (up to log factors).
Attention-based Graph Neural Network for Semi-supervised Learning
Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches.
Ranked #10 on
Graph Regression
on Lipophilicity
Learning from Comparisons and Choices
This also allows one to compute similarities among users and items to be used for categorization and search.
Collaboratively Learning Preferences from Ordinal Data
In order to predict the preferences, we want to learn the underlying model from noisy observations of the low-rank matrix, collected as revealed preferences in various forms of ordinal data.
