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Guillaume Papa

2 accepted papers

2016

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability

NeurIPS 2016poster

The problem of predicting connections between a set of data points finds many applications, in systems biology and social network analysis among others. This paper focuses on the \textit{graph reconstruction} problem, where the prediction rule is obtained by minimizing the average error over all n(n…

Cited by 10SourcePDFScholar
2015

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk

NeurIPS 2015poster

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. In this paper, we focus on how to best implement a…

Cited by 23SourcePDFScholar