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Found 4 papers, 1 papers with code
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort.
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
The problem of predicting connections between a set of data points finds many applications, in systems biology and social network analysis among others.
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
In many learning problems, ranging from clustering to ranking through metric learning, empirical estimates of the risk functional consist of an average over tuples (e. g., pairs or triplets) of observations, rather than over individual observations.
Survey schemes for stochastic gradient descent with applications to M-estimation
In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible.
