location: Orchard View Room
SILO: Data Science Institute Talks
Bio Kyle Cranmer is a professor in the Physics Department with affiliate appointments in Computer Sciences and Statistics. He is also the David R. Anderson Director of the University of Wisconsin-Madison’s Data Science Institute (DSI). Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 and …
SILO: Understanding and Leveraging Adaptive Algorithms’ Sensitivity to Change-of-Basis
Abstract Adaptive gradient methods—such as Adagrad, Adam, and their variants—have found widespread use in machine learning, signal processing, and many other settings. However many algorithms in this family are not rotationally equivariant: in this talk we examine how a simple change-of-basis in either parameter space or data space can drastically …
SILO: Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms
Abstract When optimizing a nonlinear objective, one can employ a neural network as a surrogate for the nonlinear function. However, the resulting optimization model can be time-consuming to solve globally with exact methods. As a result, local search that exploits the neural-network structure has been employed to find good solutions …