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157251 https://hdl.handle.net/2286/R.I.53675 http://rightsstatements.org/vocab/InC/1.0/ 2019 vi, 57 pages : color illustrations Masters Thesis Academic theses Text eng Barron, Trevor Ben Amor, Heni He, Jingrui Levihn, Martin Arizona State University Partial requirement for: M.S., Arizona State University, 2019 Includes bibliographical references (pages 35-39) Field of study: Computer science This thesis presents a family of adaptive curvature methods for gradient-based stochastic optimization. In particular, a general algorithmic framework is introduced along with a practical implementation that yields an efficient, adaptive curvature gradient descent algorithm. To this end, a theoretical and practical link between curvature matrix estimation and shrinkage methods for covariance matrices is established. The use of shrinkage improves estimation accuracy of the curvature matrix when data samples are scarce. This thesis also introduce several insights that result in data- and computation-efficient update equations. Empirical results suggest that the proposed method compares favorably with existing second-order techniques based on the Fisher or Gauss-Newton and with adaptive stochastic gradient descent methods on both supervised and reinforcement learning tasks. Statistics Robotics Natural gradient descent Policy gradient methods Truncated Newton methods Stochastic Processes Stochastic matrices Mathematical optimization Adaptive Curvature for Stochastic Optimization