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Found 6 papers, 2 papers with code
Complexity of Inexact Proximal Point Algorithm for minimizing convex functions with Holderian Growth
Several decades ago the Proximal Point Algorithm (PPA) started to gain a long-lasting attraction for both abstract operator theory and numerical optimization communities.
Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models
Stochastic optimization lies at the core of most statistical learning models.
Stochastic proximal splitting algorithm for composite minimization
In the large-scale or noisy contexts, when only stochastic information on the smooth part of the objective function is available, the extension of proximal gradient schemes to stochastic oracles is based on proximal tractability of the nonsmooth component and it has been deeply analyzed in the literature.
Quick survey of graph-based fraud detection methods
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles.
Community-Level Anomaly Detection for Anti-Money Laundering
Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on.
New nonasymptotic convergence rates of stochastic proximal pointalgorithm for convex optimization problems
Inspired by the scalability of alternating projection methods, we start from the (linear) regularity assumption, typically used in convex feasiblity problems to guarantee the linear convergence of stochastic alternating projection methods, and analyze a general weak linear regularity condition which facilitates convergence rate boosts in stochastic proximal point schemes.
