“If learning is the answer, what is the question?” - Yoav Shoham, Kevin Leyton-Brown, Multiagent Systems.
Paul Ruvolo has brought aboard three sophomoric “researchers” in Michael Bocamazo, Subhash Gubba, and Deniz Celik to help him with the “Efficient Lifelong Learning Algorithm”, ELLA. Lifelong learning seeks to extend the multi-task learning framework to online learning, in which new data is received sequentially and learning is transferred both forward to new tasks, and backwards to refine previous tasks.
This week our group has read over many relevant papers, discussed the algorithm behind ELLA in depth with Paul Ruvolo, and found many interesting datasets to test ELLA’s merit. For example we found the UCI Machine Learning Repository very useful in finding datasets with many features and tasks. We spent a majority of our time reading over papers that cited ELLA and papers within the lifelong learning space in general.
A major research goal is to find compelling problems that will push the boundaries of the current algorithm and further knowledge in the lifelong learning field. As the week comes to a close we are stuck with the problem of finding which problems ELLA is most suited to solve, or better, uniquely suited to solve. ELLA uses a ‘shared knowledge basis’ to encode weights learned on different tasks in an attempt to find underlying ‘basis tasks’ which can be linearly combined to evaluate new tasks. These basis tasks are vectors (of length d, the number of dimensions or attributes of each element) within the shared knowledge matrix. To build confidence in the robustness of ELLA, datasets with high dimensionality, potential for high k (number of underlying basis vectors in the shared knowledge matrix), and many tasks are needed.
Many of the datasets we found were already feature extracted in which case applying ELLA is a much easier task than identifying and extracting our own features. We plan on having at least one data set that we analyze ourselves along with multiple datasets that have been pre-extracted. As of now, we are focusing on the computer vision for our extraction and may be branching out into domains such as education research, multiagent systems, audio processing and natural language processing. Experimentation with Turtlebots and Quadcopters is also a possibility, in which we would work on control applications of ELLA, introduced in PG-ELLA. Reinforcement Learning is the basis for these applications.
During a conference call with Paul Ruvolo’s collaborators, we discussed coordination with other researchers and students working on ELLA, so these blog posts may also discuss work being done within the whole project team.