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Found 5 papers, 1 papers with code
Bandits with Abstention under Expert Advice
We study the classic problem of prediction with expert advice under bandit feedback.
Expectation-Complete Graph Representations with Homomorphisms
We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation.
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-structured data.
Active Learning of Classifiers with Label and Seed Queries
In this work we show that, by carefully combining the two types of queries, a binary classifier can be learned in time $\operatorname{poly}(n+m)$ using only $O(m^2 \log n)$ label queries and $O\big(m \log \frac{m}{\gamma}\big)$ seed queries; the result extends to $k$-class classifiers at the price of a $k! k^2$ multiplicative overhead.
Active Learning of Convex Halfspaces on Graphs
We systematically study the query complexity of learning geodesically convex halfspaces on graphs.
