Title: Second-order Optimization Made Practical Abstract: Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent.  Higher-order (preconditioned) optimization methods have become far less prevalent, despite compelling theoretical properties, due to their impractical computation, memory and communication costs.  I will present some recent theoretical, algorithmic ... Read more