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Subgradient descent for machine learning Assumptions is the expected risk, the empirical risk
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Classroom Contents
Large Scale Machine Learning and Convex Optimization - Lecture 3
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- 1 Intro
- 2 Main motivating examples
- 3 Subgradient method/descent (Shor et al., 1985)
- 4 Subgradient descent for machine learning Assumptions is the expected risk, the empirical risk
- 5 Summary: minimizing convex functions
- 6 Relationship to online learning
- 7 Stochastic subgradient "descent" /method
- 8 Convex stochastic approximation Existing work • Known global minimax rates of convergence for non-smooth problems (Nemirovsky and Yudin, 1983; Agarwal et al., 2012)
- 9 Robustness to wrong constants for = Cn
- 10 Robustness to lack of strong convexity
- 11 Beyond stochastic gradient method
- 12 Outline
- 13 Adaptive algorithm for logistic regression
- 14 Self-concordance
- 15 Least-mean-square algorithm
- 16 Markov chain interpretation of constant step sizes
- 17 Least-squares - Proof technique
- 18 Simulations - synthetic examples
