Dartmouth Events

Title: Replicability in Machine Learning

Abstract: Replicability is vital to ensuring scientific conclusions are reliable, but failures of replicability have been a major issue in nearly all scientific areas of study, and machine learning is no exception. While failures of replicability in machine learning are multifactorial, one obstacle to replication efforts is the ambiguity in whether or not a replication effort was successful when many good models exist for a task. In this talk, we will discuss a new formalization of replicability for batch and reinforcement learning algorithms, and demonstrate how to solve fundamental tasks in learning under the constraints of replicability. We will also discuss how replicability relates to other algorithmic desiderata in responsible computing, such as differential privacy.

 

Bio: Jessica Sorrell is a postdoc at the University of Pennsylvania, where she works with Aaron Roth and Michael Kearns. She completed her PhD at the University of California, San Diego, advised by Russell Impagliazzo and Daniele Micciancio. She is broadly interested in the theoretical foundations of responsible computing, and her work spans a variety of pressing issues in machine learning, such as replicability, privacy, and fairness.