A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality
暂无分享,去创建一个
[1] Rhodes,et al. Optimal stochastic linear systems with exponential performance criteria and their relation to deterministic differential games , 1973 .
[2] Angelia Nedic,et al. On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging , 2013, SIAM J. Optim..
[3] Babak Hassibi,et al. A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[4] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[5] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[6] P. Whittle. Risk-Sensitive Optimal Control , 1990 .
[7] Babak Hassibi,et al. Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization , 2018, ICLR.
[8] T. Kailath,et al. Indefinite-quadratic estimation and control: a unified approach to H 2 and H ∞ theories , 1999 .
[9] D. Jacobson,et al. Optimization of stochastic linear systems with additive measurement and process noise using exponential performance criteria , 1974 .
[10] Thomas Kailath,et al. Hoo Optimality Criteria for LMS and Backpropagation , 1993, NIPS 1993.
[11] H. Robbins. A Stochastic Approximation Method , 1951 .
[12] Dale Schuurmans,et al. General Convergence Results for Linear Discriminant Updates , 1997, COLT '97.
[13] Stephen P. Boyd,et al. Stochastic Mirror Descent in Variationally Coherent Optimization Problems , 2017, NIPS.
[14] Marc Teboulle,et al. Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..
[15] Lin Xiao,et al. Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..
[16] Claudio Gentile,et al. The Robustness of the p-Norm Algorithms , 1999, COLT '99.
[17] Nathan Srebro,et al. Characterizing Implicit Bias in Terms of Optimization Geometry , 2018, ICML.
[18] Maxim Raginsky,et al. Continuous-time stochastic Mirror Descent on a network: Variance reduction, consensus, convergence , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[19] Yurii Nesterov,et al. Primal-dual subgradient methods for convex problems , 2005, Math. Program..
[20] Nicolò Cesa-Bianchi,et al. Mirror Descent Meets Fixed Share (and feels no regret) , 2012, NIPS.
[21] J. Speyer,et al. Optimal stochastic estimation with exponential cost criteria , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.
[22] Ali H. Sayed,et al. H∞ optimality of the LMS algorithm , 1996, IEEE Trans. Signal Process..