My primary research interests are in the areas of artificial intelligence and machine learning, with a focus on the following topics:

  • Lifelong learning of multiple sequential tasks over long time scales,
  • Knowledge transfer between learning tasks, and
  • Interactive AI methods that combine system-driven active learning with extensive user-driven control over learning and reasoning processes.

I am also interested in applications of AI to medicine, search and rescue, and space exploration.

This research has also produced a number of software packages, which I make freely available for academic and not-for-profit use.


              learningLifelong Machine Learning   (Related Publications)

Lifelong learning is essential for an intelligent agent that will persist in the real world with any amount of versatility. Animals learn to solve increasingly complex tasks by continually building upon and refining their knowledge. Virtually every aspect of higher-level learning and memory involves this process of continual learning and transfer.  In contrast, most current machine learning methods take a "single-shot" approach in which knowledge is not retained between learning problems.

My research seeks to develop lifelong machine learning for intelligent agents situated for extended periods in an environment and faced with multiple tasks.  The agent will continually learn to solve multiple (possibly interleaved) tasks through a combination of knowledge transfer from previously learned models, revision of stored source knowledge from new experience, and optional guidance from external teachers or human experts.  This project is closely related to my previous work in selective knowledge transfer.  The goal of this work is to enable persistent agents to develop increasingly complex abilities over time by continually and synergistically building upon their knowledge.   Lifelong learning could substantially improve the versatility of learning systems by enabling them to quickly learn a broad range of complex tasks and adapt to changing circumstances.

This project is funded under the Office of Naval Research (ONR) Active Transfer Learning program and is joint work with Terran Lane (University of New Mexico).  For further details on this ongoing project, please contact me directly.

Selective Knowledge Transfer   (Related Publications)

              Transfer ManifoldMy dissertation research focused on the selective transfer of knowledge between learning tasks [Eaton, 2009]. In this context, a task is a single learning problem, such as learning to recognize a particular visual object. An agent, having faced multiple tasks, would have built up a repository of learned knowledge that could be used to improve future learning. I developed methods that automatically select the particular source knowledge to transfer in learning a new target task. Until my dissertation, the problem of source knowledge selection had received little attention, despite its importance to the development of robust transfer learning algorithms. My dissertation showed that proper source selection can produce dramatic improvements in transfer performance by identifying the knowledge that would best improve learning of the new task. This aspect can be measured by the transferability between tasks -- a measure, introduced in my dissertation, of the change in performance between learning with and without transfer.

I developed selective transfer methods based on this notion of transferability for two general scenarios: the transfer of individual training instances and the transfer of model components between tasks. For instance-based transfer, I developed the TransferBoost algorithm, which uses a novel form of set-based boosting to select individual source instances to augment a target task's training data [Eaton & desJardins, 2011; Eaton & desJardins, 2009]. For model-based transfer, my approach organizes the source tasks onto a manifold that captures the transferability between tasks [Eaton, desJardins & Lane in ECML 2008].  Using the basis functions of this manifold, it learns a transfer function that represents how components of previously learned models are best transferred between tasks, and uses this function to select the model components to transfer to a new target task.

Interactive Artificial Intelligence   (Related Publications)

              LearningMy research on interactive AI methods seeks to give users extensive control over reasoning and learning processes. In many critical applications, especially in military and medical domains, users will reject traditional AI automation without the ability for each result to be checked and altered by a human operator. Interactive AI methods incorporate such levels of user control to facilitate the transition of AI into these types of applications. This interactive AI paradigm combines user-driven control with the complementary system-driven approach of active learning.

In recent work, we explored interactive learning of a regression function in collaboration with the user [Eaton, Holness, & McFarlane in AAAI 2010]. The user views a scatterplot of data scored by the function and can graphically correct the score of individual instances. The system then generalizes each correction to nearby instances, as determined by a manifold underlying the data, and updates the learned function and associated scatterplot. Interactive learning based on this manifold generalizes each user adjustment to other related data in an intuitive manner that monotonically improves performance with any correction. This property of continuous improvement ensures that the system will be accepted by users, in contrast to several other learning approaches which initially overfit each correction, potentially causing users to decrease their trust in the AI components. This work was applied to an information management system used to monitor threats -- an application in which users require the ability to rapidly adjust the scoring function in response to changing requirements. This technique could also be applied to other systems used by network security analysts, stock traders, and crisis monitoring centers.


Constrained Clustering   (Related Publications)Multi-view Constrained Clustering

Constrained clustering uses background knowledge in the form of must-link constraints, which specify that two instances belong in the same cluster, and cannot-link constraints, which specify that two instances belong in different clusters, to improve the resulting clustering. My Master's thesis work [Eaton, 2005] focused on a method for propagating a given set of constraints to other data instances based on the cluster geometry, decreasing the number of constraints needed to achieve high performance. This method for constraint propagation was later used as the foundation for the first mult-view constrained clustering method that supports an incomplete mapping between views [Eaton, desJardins, & Jacob in KAIS 2012; Eaton, desJardins, & Jacob in CIKM 2010].  In this method, clustering progress in one view of the data (e.g., images) is propagated via a set of pairwise constraints to improve learning performance in another view (e.g., associated text documents).  The key contribution of this work is that it supports an incomplete mapping between views, enabling the method to be successfully applied to a larger range of applications and legacy data sets that have multiple views available for only a limited portion of the data.


              Preference LearningLearning User Preferences over Sets of Objects   (Related Publications)

In collaboration with Marie desJardins (UMBC) and Kiri Wagstaff (NASA Jet Propulsion Laboratory), I developed the DDPref framework for learning and reasoning about a user's preferences for selecting sets of objects where items in the set interact [desJardins, Eaton & Wagstaff, 2006; Wagstaff, desJardins & Eaton, 2010]. The DDPref representation captures interactions between items within a set, modeling the user's desired levels of quality and diversity of items in the set. Our approach allows a user to either manually specify a preference representation, or select example sets that represent their desired information, from which we can learn a representation of their preferences. We applied the DDPref method to identify sets of images taken by a remote Mars rover for transmission back to the user. Due to the limited communications bandwidth, it is important to send back a set of images which together captures the user's desired information. This research is also applicable to search result set creation, automatic content presentation, and targeted information acquisition.