Jordan Ash will present his Pre FPO "Towards Flexible Active and Online Learning With Neural Networks" on Friday, March 6, 2020 at 1pm in CS 302. The members of his committee are as follows: Ryan Adams (Adviser); Readers: Ryan Adams and Rob Schapire (MSR); Examiners: Barbara Engelhardt, Szymon Rusinkiewicz, and Akshay Krishnamurthy (MSR) Everyone is invited to attend his talk. The talk title and abstract follow below. Title: Towards Flexible Active and Online Learning With Neural Networks Abstract: Deep learning has elicited breakthrough successes on a wide array of machine learning tasks. Outside of the fully-supervised regime, however, many deep learning algorithms are brittle and unable to reliably perform across model architectures, dataset types, and optimization parameters. As a consequence, these algorithms are not easily usable by non-machine-learning experts, limiting their ability to meaningfully impact science and society. This talk consists of two parts. First, I’ll overview an in-progress project involving machine learning for 3D printing. I will then move to the main part of the talk, and address some nuanced pathologies around the use of deep learning for active and passive online learning. I propose a practical active learning approach for neural networks that is robust to environmental variables: Batch Active learning by Diverse Gradient Embeddings (BADGE). I also discuss the deleterious generalization effects of warm-starting the optimization of neural networks in sequential environments, why this is a major problem for deep learning, and provide a simple solution.