Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent
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[1] R. Langevin. Differential Geometry of Curves and Surfaces , 2001 .
[2] Shai Shalev-Shwartz,et al. Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..
[3] Nathan Srebro,et al. Implicit Regularization in Matrix Factorization , 2017, 2018 Information Theory and Applications Workshop (ITA).
[4] Ambuj Tewari,et al. On the Universality of Online Mirror Descent , 2011, NIPS.
[5] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[6] Andre Wibisono,et al. A variational perspective on accelerated methods in optimization , 2016, Proceedings of the National Academy of Sciences.
[7] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[8] Ruslan Salakhutdinov,et al. Geometry of Optimization and Implicit Regularization in Deep Learning , 2017, ArXiv.
[9] Nathan Srebro,et al. Characterizing Implicit Bias in Terms of Optimization Geometry , 2018, ICML.
[10] Sayan Mukherjee,et al. The Information Geometry of Mirror Descent , 2013, IEEE Transactions on Information Theory.
[11] Kaifeng Lyu,et al. Gradient Descent Maximizes the Margin of Homogeneous Neural Networks , 2019, ICLR.
[12] 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).
[13] Matus Telgarsky,et al. Margins, Shrinkage, and Boosting , 2013, ICML.
[14] Nathan Srebro,et al. Kernel and Deep Regimes in Overparametrized Models , 2019, ArXiv.
[15] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[16] Marc Teboulle,et al. Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..
[17] Eric Moulines,et al. Unifying mirror descent and dual averaging , 2019, ArXiv.
[18] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .