Stochastic approximation: from statistical origin to big-data, multidisciplinary applications
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Publication:2038304
DOI10.1214/20-STS784OpenAlexW3155576932MaRDI QIDQ2038304
Publication date: 6 July 2021
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/20-sts784
optimizationcontrolregretgradient boostingrecursive stochastic algorithmsweak greedy variable selection
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Cites Work
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- Greedy function approximation: A gradient boosting machine.
- Regularized estimation in sparse high-dimensional time series models
- A stepwise regression method and consistent model selection for high-dimensional sparse linear models
- Theory and applications of multivariate self-normalized processes
- Asymptotically efficient adaptive allocation rules
- A Newton-Raphson version of the multivariate Robbins-Monro procedure
- Rational transfer function approximation (with discussion)
- Adaptive treatment allocation and the multi-armed bandit problem
- Multivariate adaptive stochastic approximation
- Adaptive design and stochastic approximation
- Iterated least squares in multiperiod control
- Stochastic approximation methods for constrained and unconstrained systems
- On ARX(\(\infty)\) approximation
- A decision-theoretic generalization of on-line learning and an application to boosting
- Herbert Robbins and sequential analysis
- Robbins, empirical Bayes and microarrays
- Compound decision theory and empirical Bayes methods
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Weak greedy algorithms
- Statistical science in information technology and precision medicine
- Boosting for high-dimensional linear models
- Problems of identification and control
- On self tuning regulators
- Adaptive stochastic approximation by the simultaneous perturbation method
- Analyzing Convergence and Rates of Convergence of Particle Swarm Optimization Algorithms Using Stochastic Approximation Methods
- An Interview with Herbert Robbins
- Optimum Sequential Search and Approximation Methods Under Minimum Regularity Assumptions
- Particle Swarm Optimization
- Extended least squares and their applications to adaptive control and prediction in linear systems
- Consistency and asymptotic efficiency of slope estimates in stochastic approximation schemes
- Indirect stochastic adaptive control: The general delay-white noise case
- Discrete Time Stochastic Adaptive Control
- Recursive Identification and Adaptive Prediction in Linear Stochastic Systems
- Parallel Recursive Algorithms in Asymptotically Efficient Adaptive Control of Linear Stochastic Systems
- Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
- Analysis of recursive stochastic algorithms
- Introduction to Stochastic Search and Optimization
- Matching pursuits with time-frequency dictionaries
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning
- Exact moderate and large deviations for linear processes
- Experimental Determination of the Maximum of a Function
- Stochastic Estimation of the Maximum of a Regression Function
- Some aspects of the sequential design of experiments
- Sequential Minimax Search for a Maximum
- A Stochastic Approximation Method
- Approximation Methods which Converge with Probability one
- Two properties of the function cos𝑥
- On the Measure of a Random Set. II
- The contributions of Herbert Robbins to mathematical statistics
- Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems
- Stochastic approximation
- Performance bounds for parameter estimates of high-dimensional linear models with correlated errors
