Adaptive Sequential Optimization with Applications to Machine Learning

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.Comment: submitted to ICASSP 2016, extended versio

Lepidochelys kempii

Number of Pages: 8Integrative BiologyGeological Science

Characteristics of a variable spaced planar thermionic converter with a tungsten emitter and a niobium collector

Current-voltage characteristics of variable spaced thermionic converter with tungsten emitter and niobium collecto

Quantum affine modules for non-twisted affine Kac-Moody algebras

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Characteristics of a thermionic converter with a chloride vapor deposited tungsten emitter /110/ and a nickel collector

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