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Found 5 papers, 1 papers with code
Matrix Completion with Hierarchical Graph Side Information
We consider a matrix completion problem that exploits social or item similarity graphs as side information.
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs
In the achievability proof, we demonstrate that probability of error of the maximum likelihood estimator vanishes for sufficiently large number of users and items, if all sufficient conditions are satisfied.
On the Fundamental Limits of Coded Data Shuffling for Distributed Machine Learning
In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files, assuming the cached files are uncoded.
Active Learning for Top-$K$ Rank Aggregation from Noisy Comparisons
We explore an active top-$K$ ranking problem based on pairwise comparisons that are collected possibly in a sequential manner as per our design choice.
Randomized Algorithms for Comparison-based Search
This paper addresses the problem of finding the nearest neighbor (or one of the $R$-nearest neighbors) of a query object $q$ in a database of $n$ objects, when we can only use a comparison oracle.
