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Found 6 papers, 1 papers with code
Scalable Decentralized Algorithms for Online Personalized Mean Estimation
In numerous settings, agents lack sufficient data to directly learn a model.
Ranking a Set of Objects using Heterogeneous Workers: QUITE an Easy Problem
We focus on the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of unequal workers, each worker being characterized by a specific degree of reliability, which reflects her ability to rank pairs of objects.
Federated Learning under Heterogeneous and Correlated Client Availability
To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation.
Content Placement in Networks of Similarity Caches
Similarity caching systems have recently attracted the attention of the scientific community, as they can be profitably used in many application contexts, like multimedia retrieval, advertising, object recognition, recommender systems and online content-match applications.
Ranking a set of objects: a graph based least-square approach
We propose a class of non-adaptive ranking algorithms that rely on a least-squares optimization criterion for the estimation of qualities.
Selecting the top-quality item through crowd scoring
We investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values.
