DeLTA seminar by Jessica Sorrell: Replicability in Machine Learning
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Speaker
Jessica Sorrell, John Hopkins University
Title
Improving Risk Bounds with Unbounded Losses via Data-Dependent Priors
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.
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DeLTA is a research group affiliated with the Department of Computer Science at the University of Copenhagen studying diverse aspects of Machine Learning Theory and its applications, including, but not limited to Reinforcement Learning, Online Learning and Bandits, PAC-Bayesian analysis