My primary research interests are in the areas of machine learning and artificial intelligence, with a focus on the following topics:
- Lifelong learning of multiple consecutive tasks over long time scales,
- Knowledge transfer between learning tasks, including the automatic cross-domain mapping of knowledge,
- Interactive AI methods that combine system-driven active learning with extensive user-driven control over learning and reasoning processes, and
- Big data analytics through lifelong and transfer learning, with a focus on learning multiple tasks online from streaming data and adjusting for drifting task distributions.
My research applies these methods to problems in robotics, computational sustainability, and medicine.
Lifelong 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.
Under this project, we developed the Efficient Lifelong Learning Algorithm (ELLA) [Ruvolo & Eaton, ICML 2013] -- a method for online multi-task learning of consecutive tasks that has equivalent performance to batch multi-task learning with over a 1,000x speedup. ELLA learns and maintains a repository of shared knowledge, rapidly learning new task models by building upon previous knowledge. ELLA provides a variety of theoretical guarantees on performance and convergence, along with state-of-the-art performance on supervised multi-task learning problems. It also supports active task selection to intelligently choose the next task to learn in order to maximize performance [Ruvolo & Eaton, AAAI 2013].
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 / Google) and Paul Ruvolo (Olin College). For further details on this ongoing project, please see our recent publications or contact me directly.
Multi-Task Policy Gradient Methods for Robotic Control (Related Publications)
We have extended ELLA to sequential decision making through the PG-ELLA algorithm for online multi-task learning using policy gradients [Bou Ammar, Eaton, Taylor & Ruvolo, ICML 2014]. Policy gradient methods support sequential decision making with continuous state and action spaces, and have been use with great success for robotic control. Our PG-ELLA algorithm incorporates ELLA's notion of using a shared basis to transfer knowledge between multiple sequential decision making tasks, and provides a computationally efficient method of learning new control policies by building upon previously learned knowledge. Rapid learning of control policies for new systems is essential to minimize both training time as well as wear-and-tear on the robot. We applied PG-ELLA to learn control policies for a variety of dynamical systems with non-linear and highly chaotic behavior, including an application to quadrotor control.
Interactive Artificial Intelligence (Related Publications)
Selective Knowledge Transfer (Related Publications)
My dissertation research [Eaton, 2009] focused on the problem of source selection in transfer learning: given a set of previously learned source tasks, how can we select the knowledge to transfer in order to best improve learning on a new target task? In this context, a task is a single learning problem, such as learning to recognize a particular visual object. Until my dissertation, the problem of source knowledge selection for transfer learning had received little attention, despite its importance to the development of robust transfer learning algorithms. Previous methods assumed that all source tasks were relevant, including those that provided irrelevant knowledge, which can interfere with learning through the phenomenon of negative transfer.
My results showed that proper source selection can produce large improvements in transfer performance and decrease the risk of negative transfer 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 [Eaton & desJardins, 2011; Eaton & desJardins, 2009] and the transfer of model components between tasks [Eaton, desJardins & Lane in ECML 2008]. In particular, my research on model-based transfer showed that modeling the transferability relationships between tasks using a manifold provides an effective framework for source knowledge selection, providing a geometric framework for understanding how knowledge is best transferred between learning tasks.
Constrained Clustering (Related Publications)
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.
Learning User Preferences over Sets of Objects (Related Publications)
In collaboration with Marie desJardins (UMBC) and Kiri
(NASA Jet Propulsion Laboratory), I developed the DDPref
learning and reasoning
about a user's preferences for selecting sets of objects
where items in
the set interact [desJardins,
& Eaton, 2010]. The DDPref representation captures
between items within a set, modeling the user's desired
quality and diversity of items in the set. Our approach
allows a user
to either manually specify a preference representation, or
example sets that represent their desired information, from
can learn a representation of their preferences. We applied
method to identify sets of images taken by a remote Mars
transmission back to the user. Due to the limited
bandwidth, it is important to send back a set of images
captures the user's desired information. This research is
applicable to search result set creation, automatic content
presentation, and targeted information acquisition.