RI: Medium: Interactive Transfer Learning in Dynamic Environments

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Machine learning (ML) has witnessed tremendous success both in establishing firm theoretical foundations and reaching out to major applications ranging from the scientific (e.g. computational biology) to the practical (e.g. financial fraud detection, spam detection). However the reach of machine learning has been hampered by an underlying inductive framework that largely has not evolved from using only labeled instances of concepts (e.g. emails and yes/no labels on whether they are spam) and its overly simple view of the role of the user or subject matter expert (SME) as a mere provider of the labels for the training instances. However, when instructing humans, teachers provide richer information: Why is an instance of a concept a good positive example? What are key differences between instances belonging to different classes? Which properties are transient and which are invariant? Where should the learner focus attention? What does the current learning task have in common with previously acquired concepts or processes? Answers to such questions not only enrich the learning process, but they also can effectively reduce the hypothesis space and provide significant speed ups in learning than can be achieved with use of class membership feedback only. The aim of this project is to bring this kind of richer interaction into the realm of machine learning by developing frameworks as well as machine learning methods that can take advantage of fuller mixed-initiative communication. In particular, this project aims to develop ML algorithms that can exploit information from SME's such as (1) identification of landmark instances; (2) proposing rules of thumb; (3) providing feedback on similarity of instances; and (4) transfer of similarity measures themselves. This project brings to bear four streams of research: (1) algorithms based on similarity functions and landmark instances; (2) active and "pro-active" learning; (3) Bayesian active transfer learning; and (4) learning to cope with temporal evolution in the underlying data distribution. In order to reach practical results, this project focuses on challenges where these new methods are both most needed and likely to prove most effective, such as learning in dynamic environments with concept drift, and where potential for long-term transfer learning is present. Broader impacts include more effective learning by incorporating scientific domain knowledge in eScience, for instance in computational proteomics. Educational and research-community outreach includes participation of graduates and undergraduates from Howard University, for instance in yearly research gatherings involving all students on the project, and reusable open-source methods and data sets.